public void CloneTest()
{
var source = new SystemRandomSource(new Random());
var clone = source.Clone();
Assert.IsNull(clone);
}
public void TestRandomTupleArraySorting()
{
const int len = 0x1 << 10;
SystemRandomSource random = new SystemRandomSource();
int[] keys = new int[len];
int[] items = new int[len];
int[] keysCopy = new int[len];
for (int i = 0; i < keys.Length; i++)
{
keys[i] = random.Next();
keysCopy[i] = keys[i];
items[i] = -keys[i];
}
Sorting.Sort(keys, items);
for (int i = 1; i < keys.Length; i++)
{
Assert.That(keys[i] >= keys[i - 1], "Sort Order - " + i.ToString());
Assert.That(items[i], Is.EqualTo(-keys[i]), "Items Permutation - " + i.ToString());
}
for (int i = 0; i < keysCopy.Length; i++)
{
Assert.That(Array.IndexOf(keys, keysCopy[i]) >= 0, "All keys still there - " + i.ToString());
}
}
public void TestRandomTripleArraySorting()
{
const int len = 0x1 << 10;
SystemRandomSource random = new SystemRandomSource();
int[] keys = new int[len];
int[] items1 = new int[len];
int[] items2 = new int[len];
int[] keysCopy = new int[len];
for (int i = 0; i < keys.Length; i++)
{
keys[i] = random.Next();
keysCopy[i] = keys[i];
items1[i] = -keys[i];
items2[i] = keys[i] >> 2;
}
Sorting.Sort(keys, items1, items2);
for (int i = 1; i < keys.Length; i++)
{
Assert.IsTrue(keys[i] >= keys[i - 1], "Sort Order - " + i.ToString());
Assert.AreEqual(-keys[i], items1[i], "Items1 Permutation - " + i.ToString());
Assert.AreEqual(keys[i] >> 2, items2[i], "Items2 Permutation - " + i.ToString());
}
for (int i = 0; i < keysCopy.Length; i++)
{
Assert.IsTrue(Array.IndexOf(keys, keysCopy[i]) >= 0, "All keys still there - " + i.ToString());
}
}
/// <summary>
/// Sets this instance's AmiabilityLevel
/// </summary>
/// <param name="seed">Seed based on player and slice</param>
private void SetLevel(int seed)
{
uniformRandSeed = new SystemRandomSource(seed);
binomialRandSeed = new SystemRandomSource(seed); // Doesn't matter if it's the same seed
uniformDist = new DiscreteUniform(0, Enum.GetNames(typeof(AmiabilityLevel)).Length - 1, uniformRandSeed);
AmiabilityLevel = (AmiabilityLevel)uniformDist.Sample();
}
void Awake()
{
signalControl = new ControlSignal();
path = "Save/DroneSession/" + "Task-" + task + "/Seed-" + fromSeed + "/";
rndGenerator = new SystemRandomSource(fromSeed);
taskObject = (DroneTask)Activator.CreateInstance(Type.GetType("Lexmou.MachineLearning.Drone" + task), rndGenerator, task);
mlp = new MultiLayerMathsNet(fromSeed, null, taskObject.shapes, 1, 0);
float[] floatArr = new float[taskObject.individualSize];
//float[] floatArr = new float[] {0,-0.33f,0,0,-0.33f,0,0,0,0,0,0.5f,0,0,-1,0,0,-1,0,0,0.5f,0,1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,0.5f};
Genetic.LoadBest(path, fromGeneration, floatArr);
BuildCustomWeights(mlp.weights, taskObject.shapes, Vector <float> .Build.DenseOfArray(floatArr));
//Debug.Log(mlp.weights[0]);
/*if (stabilizationGeneration != 0)
* {
* Debug.Log("Gene Move");
* gene = new Genetic(stabilizationSeed, null, 100, 40, 1.0f, 0.1f, 0.1f, 0.1f, 0.1f, "Save/DroneSession/Task-stabilization/", false);
* }
* if (moveGeneration != 0)
* {
* Debug.Log("Gene Move");
* gene = new Genetic(moveSeed, null, 100, 52, 1.0f, 0.1f, 0.1f, 0.1f, 0.1f, "Save/DroneSession/Task-move/", false);
* }
* deltaDistribution = new ContinuousUniform(-2, 2);
* tmpBuildCustomWeights = new List<Matrix<float>>();*/
Restart();
}
public void TestRandomTupleListSorting()
{
const int len = 0x1 << 10;
SystemRandomSource random = new SystemRandomSource();
List <int> keys = new List <int>(len);
List <int> items = new List <int>(len);
int[] keysCopy = new int[len];
for (int i = 0; i < len; i++)
{
int value = random.Next();
keys.Add(value);
keysCopy[i] = value;
items.Add(-value);
}
Sorting.Sort(keys, items);
for (int i = 1; i < len; i++)
{
Assert.That(keys[i] >= keys[i - 1], "Sort Order - " + i.ToString());
Assert.That(items[i], Is.EqualTo(-keys[i]), "Items Permutation - " + i.ToString());
}
for (int i = 0; i < keysCopy.Length; i++)
{
Assert.That(keys.IndexOf(keysCopy[i]) >= 0, "All keys still there - " + i.ToString());
}
}
/// <summary>
/// Sets a Region's wealth based on tech level and base region's wealth. Tech level is fed to
/// a switch which determines alpha/beta of the Beta distribution. A sample is then called which
/// is between 0 and 1. That value is then multiplied by the maxWealth to determine the region's
/// actual wealth.
/// </summary>
/// <param name="seed">Seed based on player and slice</param>
/// <param name="techLevel">Region's tech level</param>
/// <param name="maxWealth">Max wealth for this Region</param>
public Wealth(int seed, TechLevel techLevel, UInt64 maxWealth)
{
randSeed = new SystemRandomSource(seed);
FillDistribution(techLevel);
Weight = betaDist.Sample();
RegionWealth = (UInt64)(Weight * maxWealth);
}
internal override void GenerateCurve()
{
//create the senseCurve, the start of the curve and start populating it with random values
List <SensitivityPoint> sensCurve = new List <SensitivityPoint>();
SensitivityPoint firstPoint = new SensitivityPoint(0, sensMean);
sensCurve.Add(firstPoint);
var seededRandom = new SystemRandomSource(42);//creates a random source with the seed "42"
for (double timecode = curveTimestep; timecode < this.lenght; timecode += curveTimestep)
{
double sensDirection = seededRandom.NextDouble(); //create a random double to determine if sense is going to be faster or slower
if (sensDirection >= 0.5) //sens will be faster
{
double randomSens = sensMean + seededRandom.NextDouble() * (sensMax - sensMean); //generates a random value in the range of (basesens:maxsens)
SensitivityPoint sensPoint = new SensitivityPoint(timecode, randomSens);
sensCurve.Add(sensPoint);
}
else // sensDirection <0.5 -> sens will be slower
{
double randomSens = sensMin + seededRandom.NextDouble() * (sensMean - sensMin);
SensitivityPoint sensPoint = new SensitivityPoint(timecode, randomSens);
sensCurve.Add(sensPoint);
}
}
SensitivityPoint finalSensPoint = new SensitivityPoint(this.lenght, 1);//Make sure the curve ends at base sens
sensCurve.Add(finalSensPoint);
base.sensCurve = sensCurve;
}
public DroneMove(SystemRandomSource rndGenerator, string fromTask) : base(rndGenerator)
{
angleRandomRotation = new float[7] {
2, 10, 20, 30, 40, 50, 60
};
medianThreshold = new float[7] {
70, 70, 70, 70, 70, 70, 70
};
bestThreshold = new float[7] {
170, 170, 170, 170, 170, 170, 170
};
if (fromTask == "Stabilization")
{
rowIndex = 12;
}
else if (fromTask == "Move")
{
rowIndex = 0;
}
this.fromTask = fromTask;
deltaDistribution = new ContinuousUniform(-5, 5, rndGenerator);
shapes = new List <int>()
{
12, 4
};
signal = new UCSignal(shapes[0]);
individualSize = 0;
for (int i = 0; i < shapes.Count - 1; i++)
{
individualSize += (shapes[i] + 1) * shapes[i + 1];
}
}
public void CombineUnweighted()
{
var rnd = new SystemRandomSource(10);
var a = Generate.Random(200, new Erlang(2, 0.2, rnd)).Select(datum => System.Tuple.Create(1.0, datum)).ToArray();
var b = Generate.Random(100, new Beta(1.2, 1.4, rnd)).Select(datum => System.Tuple.Create(1.0, datum)).ToArray();
var c = Generate.Random(150, new Rayleigh(0.8, rnd)).Select(datum => System.Tuple.Create(1.0, datum)).ToArray();
var d = a.Concat(b).Concat(c);
var direct = d.Select(datum => datum.Item2).ToArray();
var x = new RunningWeightedStatistics(d);
var y = new RunningWeightedStatistics(a);
y.PushRange(b);
y.PushRange(c);
var za = new RunningWeightedStatistics(a);
var zb = new RunningWeightedStatistics(b);
var zc = new RunningWeightedStatistics(c);
var z = za + zb + zc;
Assert.That(x.Mean, Is.EqualTo(direct.Mean()).Within(1e-12), "Mean Reference");
Assert.That(y.Mean, Is.EqualTo(x.Mean).Within(1e-12), "Mean y");
Assert.That(z.Mean, Is.EqualTo(x.Mean).Within(1e-12), "Mean z");
Assert.That(x.Variance, Is.EqualTo(direct.Variance()).Within(1e-12), "Variance Reference");
Assert.That(y.Variance, Is.EqualTo(x.Variance).Within(1e-12), "Variance y");
Assert.That(z.Variance, Is.EqualTo(x.Variance).Within(1e-12), "Variance z");
Assert.That(x.PopulationVariance, Is.EqualTo(direct.PopulationVariance()).Within(1e-12), "PopulationVariance Reference");
Assert.That(y.PopulationVariance, Is.EqualTo(x.PopulationVariance).Within(1e-12), "PopulationVariance y");
Assert.That(z.PopulationVariance, Is.EqualTo(x.PopulationVariance).Within(1e-12), "PopulationVariance z");
Assert.That(x.StandardDeviation, Is.EqualTo(direct.StandardDeviation()).Within(1e-12), "StandardDeviation Reference");
Assert.That(y.StandardDeviation, Is.EqualTo(x.StandardDeviation).Within(1e-12), "StandardDeviation y");
Assert.That(z.StandardDeviation, Is.EqualTo(x.StandardDeviation).Within(1e-12), "StandardDeviation z");
Assert.That(x.PopulationStandardDeviation, Is.EqualTo(direct.PopulationStandardDeviation()).Within(1e-12), "PopulationStandardDeviation Reference");
Assert.That(y.PopulationStandardDeviation, Is.EqualTo(x.PopulationStandardDeviation).Within(1e-12), "PopulationStandardDeviation y");
Assert.That(z.PopulationStandardDeviation, Is.EqualTo(x.PopulationStandardDeviation).Within(1e-12), "PopulationStandardDeviation z");
Assert.That(x.Skewness, Is.EqualTo(direct.Skewness()).Within(1e-12), "Skewness Reference (not independent!)");
Assert.That(y.Skewness, Is.EqualTo(x.Skewness).Within(1e-12), "Skewness y");
Assert.That(z.Skewness, Is.EqualTo(x.Skewness).Within(1e-12), "Skewness z");
Assert.That(x.PopulationSkewness, Is.EqualTo(direct.PopulationSkewness()).Within(1e-12), "PopulationSkewness Reference (not independent!)");
Assert.That(y.PopulationSkewness, Is.EqualTo(x.PopulationSkewness).Within(1e-12), "PopulationSkewness y");
Assert.That(z.PopulationSkewness, Is.EqualTo(x.PopulationSkewness).Within(1e-12), "PopulationSkewness z");
Assert.That(x.Kurtosis, Is.EqualTo(direct.Kurtosis()).Within(1e-12), "Kurtosis Reference (not independent!)");
Assert.That(y.Kurtosis, Is.EqualTo(x.Kurtosis).Within(1e-12), "Kurtosis y");
Assert.That(z.Kurtosis, Is.EqualTo(x.Kurtosis).Within(1e-12), "Kurtosis z");
Assert.That(x.PopulationKurtosis, Is.EqualTo(direct.PopulationKurtosis()).Within(1e-12), "PopulationKurtosis Reference (not independent!)");
Assert.That(y.PopulationKurtosis, Is.EqualTo(x.PopulationKurtosis).Within(1e-12), "PopulationKurtosis y");
Assert.That(z.PopulationKurtosis, Is.EqualTo(x.PopulationKurtosis).Within(1e-12), "PopulationKurtosis z");
}
public void CustomResetSpeedTest()
{
int iter = 10000;
float initialValueWeights = 1.0f;
mlp.Reset(initialValueWeights, true);
mlpMN.Reset(true);
SystemRandomSource rndGenerator = new SystemRandomSource(seed);
List <Matrix <float> > weightsMN = new List <Matrix <float> >();
List <float[, ]> weights = new List <float[, ]>();
for (int i = 0; i < shapes.Count - 1; i++)
{
weightsMN.Add(Matrix <float> .Build.Random(mlpMN.layers[i + 1].RowCount, mlpMN.layers[i].RowCount, new ContinuousUniform(-initialValueWeights, initialValueWeights, rndGenerator)));
}
for (int i = 0; i < shapes.Count - 1; i++)
{
weights.Add(new float[mlp.layers[i].GetLength(1), mlp.layers[i + 1].GetLength(1)]);
}
for (int i = 0; i < shapes.Count - 1; i++)
{
for (int j = 0; j < weights[i].GetLength(0); j++)
{
for (int k = 0; k < weights[i].GetLength(1); k++)
{
weights[i][j, k] = (float)ContinuousUniform.Sample(rndGenerator, -initialValueWeights, initialValueWeights);
}
}
}
var watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < iter; i++)
{
mlpMN.Reset(false, weightsMN);
}
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
Debug.Log("Reset Time MathNet: " + elapsedMs);
watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < iter; i++)
{
mlp.Reset(initialValueWeights, false, weights);
}
watch.Stop();
elapsedMs = watch.ElapsedMilliseconds;
Debug.Log("Reset Time Array: " + elapsedMs);
Assert.AreEqual(mlp.weights, weights);
Assert.AreEqual(mlpMN.weights, weightsMN);
}
/// <summary>
/// Create random samples, uniform between 0 and 1.
/// Faster than other methods but with reduced guarantees on randomness.
/// </summary>
public static double[] Uniform(int length)
{
if (length < 0)
{
throw new ArgumentOutOfRangeException(nameof(length));
}
return(SystemRandomSource.FastDoubles(length));
}
private static int CalNumber()
{
{
Random random = new SystemRandomSource();
decimal MahjongNumber = (random.NextDecimal() * 42) + 1;
Math.Round(MahjongNumber);
return((int)MahjongNumber);
}
}
/**
*
*/
public Genetic(int seed, SystemRandomSource rndGenerator, int populationSize, int individualSize, float initialValueWeights, float mutationRate = 0.1f, float randomIndividualsRate = 0.05f, float bestIndividualsRate = 0.05f, float emptyRate = 0.0f, string path = "", bool save = true)
{
this.save = save;
tmp = Matrix <float> .Build.Dense(individualSize, populationSize);
this.path = path;
this.bestScore = 0.0f;
this.generation = 1;
this.seed = seed;
this.initialValueWeights = initialValueWeights;
if (rndGenerator != null)
{
this.rndGenerator = rndGenerator;
}
else
{
this.rndGenerator = new SystemRandomSource(seed);
}
distribution = new ContinuousUniform(-initialValueWeights, initialValueWeights, rndGenerator);
subDistribution = new ContinuousUniform(-0.01, 0.01, rndGenerator);
drawDistribution = new ContinuousUniform(0.0f, 1.0f, rndGenerator);
this.populationSize = populationSize;
this.individualSize = individualSize;
this.population = Matrix <float> .Build.Random(individualSize, populationSize, distribution);
for (int i = 0; i < populationSize; i++)
{
distEmpty = Combinatorics.GenerateCombination(individualSize, Mathf.RoundToInt(emptyRate * individualSize), rndGenerator);
for (int j = 0; j < individualSize; j++)
{
if (distEmpty[j])
{
population[j, i] = 0.000f;
}
}
}
this.evaluations = Vector <float> .Build.Dense(populationSize);
this.sumEvaluations = 0.0f;
this.mutationRate = mutationRate;
this.randomIndividualsRate = randomIndividualsRate;
this.bestIndividualsRate = bestIndividualsRate;
this.emptyRate = emptyRate;
if (save)
{
writer = UIO.CreateStreamWriter(GeneratePath(), "GeneticResults.csv", false);
UIO.WriteLine(writer, "Generation;Best;Mean;Std Deviation;Median");
}
indexPermutation = Combinatorics.GeneratePermutation(populationSize);
}
// Use this for initialization
public NoiseSensor(float mean, float stdDevWhite, float stdDevBrown, float deltaT, SystemRandomSource rndGenerator)
{
this.rndGenerator = rndGenerator;
this.mean = mean;
this.stdDevWhite = stdDevWhite;
this.stdDevBrown = stdDevBrown;
this.deltaT = deltaT;
normal = new Normal(mean, stdDevWhite, rndGenerator);
stdNormal = new Normal(0.0f, 1.0f, rndGenerator);
}
private void ApplyAmiablePop()
{
amiableRandSeed = new SystemRandomSource(Region.Seed);
Tuple <double, double> alphaBeta = Amiability.Amiability.GetDistributionAlphaBeta(Region.Amiability.AmiabilityLevel);
amiableDist = new Beta(alphaBeta.Item1, alphaBeta.Item2, amiableRandSeed);
double amiableWeight = amiableDist.Sample();
Pop += (UInt64)(amiableWeight * amiableMultiplier * maxPop);
}
private void ApplyTechPop()
{
techRandSeed = new SystemRandomSource(Region.Seed);
Tuple <double, double> alphaBeta = BetaTechDist.GetDistributionAlphaBeta(Region.TechLevel);
techDist = new Beta(alphaBeta.Item1, alphaBeta.Item2, techRandSeed);
double techWeight = techDist.Sample();
Pop += (UInt64)(techWeight * techMultiplier * maxPop);
}
public void MetropolisConstructor()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
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);
}
/// <summary>
/// Generate samples by sampling a function at samples from a probability distribution, uniform between 0 and 1.
/// Faster than other methods but with reduced guarantees on randomness.
/// </summary>
public static T[] UniformMap <T>(int length, Func <double, T> map)
{
if (length < 0)
{
throw new ArgumentOutOfRangeException(nameof(length));
}
var samples = SystemRandomSource.FastDoubles(length);
return(Map(samples, map));
}
public void SetUp()
{
seed = 65;
geneSeed = new Genetic(seed, null, 2, 10, initialValueWeights, 0.1f, 0.05f, 0.05f, 0, "", false);
initialValueWeights = 1.0f;
//Debug.Log(geneSeed.ToString());
//System.Threading.Thread.Sleep(10000);
SystemRandomSource rndGenerator = new SystemRandomSource(seed);
geneRndGenerator = new Genetic(seed, rndGenerator, 2, 10, initialValueWeights, 0.1f, 0.05f, 0.05f, 0, "", false);
}
/// <summary>
/// Generate samples by sampling a function at samples from a probability distribution, uniform between 0 and 1.
/// Faster than other methods but with reduced guarantees on randomness.
/// </summary>
public static T[] UniformMap <T>(int length, Func <double, T> map)
{
var samples = SystemRandomSource.Doubles(length);
var data = new T[length];
for (int i = 0; i < data.Length; i++)
{
data[i] = map(samples[i]);
}
return(data);
}
public void SampleArrayTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
var ms = new MetropolisSampler<double>(0.2, normal.Density, x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
ms.Sample(5);
}
private int CalculateProcessDuration(int to)
{
SystemRandomSource randomNumberGenerator = SystemRandomSource.Default;
//double rand = randomNumberGenerator.NextDouble();
//if ((rand = Math.Round(rand, 5)) == 1) rand = .99999;
//double randomNumber = rand;
//double x = -Math.Log(1.0 - randomNumber) * S[to];
//int ret = (int)Math.Ceiling(x);
//return ret;
return((int)Math.Ceiling(-Math.Log(1.0 - randomNumberGenerator.NextDouble()) * S[to]));
}
public void SampleTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
var ms = new MetropolisSampler <double>(0.2, normal.Density, x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
ms.Sample();
}
/// <summary>
/// Gets a normal-like distribution from the 2D array
/// </summary>
/// <param name="rand">Random source component</param>
/// <returns>Array of floats that sum <= 1</returns>
public static float[] GetNormalDistribution(SystemRandomSource rand)
{
float[][] ret_val =
{
new float[] { 75.0f, 17.0f, 5.0f, 2.0f, 1.0f },
new float[] { 68.2f, 27.2f, 4.2f, 0.2f, 0.2f },
new float[] { 52.6875f, 25.0f, 12.75f, 6.375f, 3.1875f },
new float[] { 39.4f, 30.0f, 18.4f, 8.8f, 3.4f }
};
return(ret_val[rand.Next(ret_val.Length)]);
}
public void SampleTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
var ms = new MetropolisHastingsSampler<double>(0.2, normal.Density, (x, y) => Normal.PDF(x, 0.1, y), x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
ms.Sample();
}
/// <summary>
/// Generate samples by sampling a function at sample pairs from a probability distribution, uniform between 0 and 1.
/// Faster than other methods but with reduced guarantees on randomness.
/// </summary>
public static T[] UniformMap2 <T>(int length, Func <double, double, T> map)
{
if (length < 0)
{
throw new ArgumentOutOfRangeException("length");
}
var samples1 = SystemRandomSource.FastDoubles(length);
var samples2 = SystemRandomSource.FastDoubles(length);
return(Map2(samples1, samples2, map));
}
public void SampleArrayTest()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
var ms = new MetropolisHastingsSampler <double>(0.2, normal.Density, (x, y) => Normal.PDF(x, 0.1, y), x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
ms.Sample(5);
}
public void MetropolisConstructor()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
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);
}
/// <summary>
/// Samples a sequence of Poisson distributed random variables.
/// </summary>
/// <param name="lambda">The lambda (λ) parameter of the Poisson distribution. Range: λ > 0.</param>
/// <returns>a sequence of samples from the distribution.</returns>
public static IEnumerable <int> Samples(double lambda)
{
if (!(lambda > 0.0))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
SystemRandomSource rnd = SystemRandomSource.Default;
while (true)
{
yield return(SampleUnchecked(rnd, lambda));
}
}
/// <summary>
/// Samples a sequence of uniformly distributed random variables.
/// </summary>
/// <param name="lower">Lower bound. Range: lower ≤ upper.</param>
/// <param name="upper">Upper bound. Range: lower ≤ upper.</param>
/// <returns>a sequence of samples from the discrete uniform distribution.</returns>
public static IEnumerable <int> Samples(int lower, int upper)
{
if (!(lower <= upper))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
SystemRandomSource rnd = SystemRandomSource.Default;
while (true)
{
yield return(SampleUnchecked(rnd, lower, upper));
}
}
void Awake()
{
SetParametersFromCommandLine();
Debug.Log("Awake Session");
Build();
Time.timeScale = timeScale;
BuildHUD();
BuildSessionWriter();
nextUpdate = intervalUpdate;
rndGenerator = new SystemRandomSource(seed);
Reset();
CloseSessionWriter();
}
/// <summary>
/// Samples a sequence of binomially distributed random variable.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="p">The success probability (p) in each trial. Range: 0 ≤ p ≤ 1.</param>
/// <param name="n">The number of trials (n). Range: n ≥ 0.</param>
/// <returns>a sequence of successes in <paramref name="n"/> trials.</returns>
public static IEnumerable <int> Samples(double p, int n)
{
if (!(p >= 0.0 && p <= 1.0 && n >= 0))
{
throw new ArgumentException(Resources.InvalidDistributionParameters);
}
SystemRandomSource rnd = SystemRandomSource.Default;
while (true)
{
yield return(SampleUnchecked(rnd, p, n));
}
}
public void MetropolisHastingsConstructor()
{
var normal = new Normal(0.0, 1.0);
var rnd = new SystemRandomSource(1);
var ms = new MetropolisHastingsSampler<double>(0.2, normal.Density, (x, y) => Normal.PDF(x, 0.1, y), x => Normal.Sample(rnd, x, 0.1), 10)
{
RandomSource = rnd
};
Assert.IsNotNull(ms.RandomSource);
ms.RandomSource = new System.Random(0);
Assert.IsNotNull(ms.RandomSource);
}
public void SamplesFollowsCorrectDistribution()
{
Random rnd = new SystemRandomSource(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 SystemRandomSource(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 Combine()
{
var rnd = new SystemRandomSource(10);
var a = Generate.Random(200, new Erlang(2, 0.2, rnd));
var b = Generate.Random(100, new Beta(1.2, 1.4, rnd));
var c = Generate.Random(150, new Rayleigh(0.8, rnd));
var d = a.Concat(b).Concat(c).ToArray();
var x = new RunningStatistics(d);
var y = new RunningStatistics(a);
y.PushRange(b);
y.PushRange(c);
var za = new RunningStatistics(a);
var zb = new RunningStatistics(b);
var zc = new RunningStatistics(c);
var z = za + zb + zc;
Assert.That(x.Mean, Is.EqualTo(d.Mean()).Within(1e-12), "Mean Reference");
Assert.That(y.Mean, Is.EqualTo(x.Mean).Within(1e-12), "Mean y");
Assert.That(z.Mean, Is.EqualTo(x.Mean).Within(1e-12), "Mean z");
Assert.That(x.Variance, Is.EqualTo(d.Variance()).Within(1e-12), "Variance Reference");
Assert.That(y.Variance, Is.EqualTo(x.Variance).Within(1e-12), "Variance y");
Assert.That(z.Variance, Is.EqualTo(x.Variance).Within(1e-12), "Variance z");
Assert.That(x.PopulationVariance, Is.EqualTo(d.PopulationVariance()).Within(1e-12), "PopulationVariance Reference");
Assert.That(y.PopulationVariance, Is.EqualTo(x.PopulationVariance).Within(1e-12), "PopulationVariance y");
Assert.That(z.PopulationVariance, Is.EqualTo(x.PopulationVariance).Within(1e-12), "PopulationVariance z");
Assert.That(x.StandardDeviation, Is.EqualTo(d.StandardDeviation()).Within(1e-12), "StandardDeviation Reference");
Assert.That(y.StandardDeviation, Is.EqualTo(x.StandardDeviation).Within(1e-12), "StandardDeviation y");
Assert.That(z.StandardDeviation, Is.EqualTo(x.StandardDeviation).Within(1e-12), "StandardDeviation z");
Assert.That(x.PopulationStandardDeviation, Is.EqualTo(d.PopulationStandardDeviation()).Within(1e-12), "PopulationStandardDeviation Reference");
Assert.That(y.PopulationStandardDeviation, Is.EqualTo(x.PopulationStandardDeviation).Within(1e-12), "PopulationStandardDeviation y");
Assert.That(z.PopulationStandardDeviation, Is.EqualTo(x.PopulationStandardDeviation).Within(1e-12), "PopulationStandardDeviation z");
Assert.That(x.Skewness, Is.EqualTo(d.Skewness()).Within(1e-12), "Skewness Reference (not independent!)");
Assert.That(y.Skewness, Is.EqualTo(x.Skewness).Within(1e-12), "Skewness y");
Assert.That(z.Skewness, Is.EqualTo(x.Skewness).Within(1e-12), "Skewness z");
Assert.That(x.PopulationSkewness, Is.EqualTo(d.PopulationSkewness()).Within(1e-12), "PopulationSkewness Reference (not independent!)");
Assert.That(y.PopulationSkewness, Is.EqualTo(x.PopulationSkewness).Within(1e-12), "PopulationSkewness y");
Assert.That(z.PopulationSkewness, Is.EqualTo(x.PopulationSkewness).Within(1e-12), "PopulationSkewness z");
Assert.That(x.Kurtosis, Is.EqualTo(d.Kurtosis()).Within(1e-12), "Kurtosis Reference (not independent!)");
Assert.That(y.Kurtosis, Is.EqualTo(x.Kurtosis).Within(1e-12), "Kurtosis y");
Assert.That(z.Kurtosis, Is.EqualTo(x.Kurtosis).Within(1e-12), "Kurtosis z");
Assert.That(x.PopulationKurtosis, Is.EqualTo(d.PopulationKurtosis()).Within(1e-12), "PopulationKurtosis Reference (not independent!)");
Assert.That(y.PopulationKurtosis, Is.EqualTo(x.PopulationKurtosis).Within(1e-12), "PopulationKurtosis y");
Assert.That(z.PopulationKurtosis, Is.EqualTo(x.PopulationKurtosis).Within(1e-12), "PopulationKurtosis z");
}