public void readWorkCenters()
{
using (var reader = new StreamReader(Path.Combine(DirectoryInputCSVs, "WorkCenters.csv")))
{
int row = 1;
while (!reader.EndOfStream)
{
string[] values = reader.ReadLine().Split(',');
if (row != 1)
{
WaferFabSettings.WorkCenters.Add(values[0]);
WaferFabSettings.WCServiceTimeDistributions.Add(values[0], new ExponentialDistribution(Convert.ToDouble(values[1])));
Distribution servDist = new ExponentialDistribution(Convert.ToDouble(values[1]));
if (values.Length > 3) // temporary adding x identical machines
{
WaferFabSettings.WCMachines.Add(values[0], Convert.ToInt32(values[3]));
}
WaferFabSettings.WCDispatcherTypes.Add(values[0], DispatcherBase.Type.RANDOM);
}
row++;
}
}
}
//End of ui.cs file Contents
//-------------------------------------------------------------------------
//Begin of Random.cs file contents
/// <summary>
/// Initializes the random-number generator with a specific seed.
/// </summary>
public void Initialize(uint seed)
{
RandomNumberGenerator = new MT19937Generator(seed);
betaDist = new BetaDistribution(RandomNumberGenerator);
betaPrimeDist = new BetaPrimeDistribution(RandomNumberGenerator);
cauchyDist = new CauchyDistribution(RandomNumberGenerator);
chiDist = new ChiDistribution(RandomNumberGenerator);
chiSquareDist = new ChiSquareDistribution(RandomNumberGenerator);
continuousUniformDist = new ContinuousUniformDistribution(RandomNumberGenerator);
erlangDist = new ErlangDistribution(RandomNumberGenerator);
exponentialDist = new ExponentialDistribution(RandomNumberGenerator);
fisherSnedecorDist = new FisherSnedecorDistribution(RandomNumberGenerator);
fisherTippettDist = new FisherTippettDistribution(RandomNumberGenerator);
gammaDist = new GammaDistribution(RandomNumberGenerator);
laplaceDist = new LaplaceDistribution(RandomNumberGenerator);
lognormalDist = new LognormalDistribution(RandomNumberGenerator);
normalDist = new NormalDistribution(RandomNumberGenerator);
paretoDist = new ParetoDistribution(RandomNumberGenerator);
powerDist = new PowerDistribution(RandomNumberGenerator);
rayleighDist = new RayleighDistribution(RandomNumberGenerator);
studentsTDist = new StudentsTDistribution(RandomNumberGenerator);
triangularDist = new TriangularDistribution(RandomNumberGenerator);
weibullDist = new WeibullDistribution(RandomNumberGenerator);
poissonDist = new PoissonDistribution(RandomNumberGenerator);
// generator.randomGenerator = new MT19937Generator(seed);
}
public void FitTest1()
{
double[] values =
{
0, 1, 2, 4, 2, 3, 5, 7, 4, 3, 2, 1, 4,
};
var exp = new ExponentialDistribution();
exp.Fit(values);
string actual;
var cultureInfo = CultureInfo.GetCultureInfo("fr-FR");
#if NETCORE
System.Globalization.CultureInfo.DefaultThreadCurrentCulture = cultureInfo;
System.Globalization.CultureInfo.DefaultThreadCurrentUICulture = cultureInfo;
#else
Thread.CurrentThread.CurrentCulture = cultureInfo;
#endif
actual = exp.ToString("G3", CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342)", actual);
actual = exp.ToString("G3");
Assert.AreEqual("Exp(x; λ = 0,342)", actual);
actual = exp.ToString(CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342105263157895)", actual);
actual = exp.ToString();
Assert.AreEqual("Exp(x; λ = 0,342105263157895)", actual);
}
protected override void EndProcessing()
{
var dist = new ExponentialDistribution(Rate);
var obj = DistributionHelper.AddConvinienceMethods(dist);
WriteObject(obj);
}
private EstPoint[] EstBruteforce(double gammaFrom, double gammaTo, int steps, bool minus = false)
{
EstPoint[] estPoints = new EstPoint[steps];
GammaBounds(ref gammaFrom, ref gammaTo);
double step = (gammaTo - gammaFrom) / (steps - 1);
var opts = new ParallelOptions {
MaxDegreeOfParallelism = Environment.ProcessorCount * 4
};
Parallel.For(0, steps, opts, g => {
double gamma = gammaFrom + g * step;
var distr = MakeDistr(gamma);
var rnd2 = new ExponentialDistribution(distr.Alpha);
int w = 0;
for (int batch = 0; batch < BatchCount; batch++)
{
double[] array = rnd2.Generate(BatchSize);
double delta = Hill(distr, array) - distr.AlphaRev;
if (!minus && delta > distr.Bn || minus && delta < -distr.Bn)
{
w++;
}
}
double w0 = (double)w / BatchCount;
double p0 = 1; //NormalDistribution.Standard.InverseDistributionFunction(gamma);
double d = p0 * Math.Sqrt(w0 * (1 - w0) / BatchCount);
estPoints[g] = new EstPoint(w0, d, gamma);
});
return(estPoints);
}
public void ConstructorTest()
{
ExponentialDistribution n = new ExponentialDistribution(3.42521);
Assert.AreEqual(3.42521, n.Rate);
Assert.AreEqual(0.29195290215782393, n.Mean);
Assert.AreEqual(0.085236497078375897, n.Variance);
}
public void ExponentialFitUncertainty()
{
// check that the uncertainty in reported fit parameters is actually meaningful
// it should be the standard deviation of fit parameter values in a sample of many fits
// define a population distribution
ExponentialDistribution distribution = new ExponentialDistribution(4.0);
// draw a lot of samples from it; fit each sample and
// record the reported parameter value and error of each
Sample values = new Sample();
Sample uncertainties = new Sample();
for (int i = 0; i < 128; i++)
{
Sample sample = SampleTest.CreateSample(distribution, 8, i);
ExponentialFitResult fit = ExponentialDistribution.FitToSample(sample);
UncertainValue lambda = fit.Parameters[0].Estimate;
values.Add(lambda.Value);
uncertainties.Add(lambda.Uncertainty);
}
// the reported values should agree with the source distribution
Assert.IsTrue(values.PopulationMean.ConfidenceInterval(0.95).ClosedContains(distribution.Mean));
// the reported errors should agree with the standard deviation of the reported parameters
Assert.IsTrue(values.PopulationStandardDeviation.ConfidenceInterval(0.95).ClosedContains(uncertainties.Mean));
}
public void Next()
{
var mean = 2.0;
var count = 100000;
var values = Enumerable.Range(0, count)
.Select(i => ExponentialDistribution.Next(mean))
.OrderBy(x => x)
.ToArray();
Console.WriteLine($"Mean of Values: {values.Average()}");
var largeValues = values.SkipWhile(x => x < mean).ToArray();
// 1/e
Console.WriteLine($"Large Count Ratio: {(double)largeValues.Length / count}");
// 2/e
Console.WriteLine($"Large Sum Ratio: {largeValues.Sum() / (mean * count)}");
Console.WriteLine();
foreach (var g in values.GroupBy(x => (int)x))
{
Console.WriteLine($"{g.Key}: {g.Count()}");
}
Console.WriteLine();
foreach (var g in values.GroupBy(x => Math.Floor(x * 10) / 10))
{
Console.WriteLine($"{g.Key:F1}: {g.Count()}");
}
}
public void FitTest1()
{
double[] values =
{
0, 1, 2, 4, 2, 3, 5, 7, 4, 3, 2, 1, 4,
};
var exp = new ExponentialDistribution();
exp.Fit(values);
string actual;
Thread.CurrentThread.CurrentCulture = CultureInfo.GetCultureInfo("fr-FR");
actual = exp.ToString("G3", CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342)", actual);
actual = exp.ToString("G3");
Assert.AreEqual("Exp(x; λ = 0,342)", actual);
actual = exp.ToString(CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342105263157895)", actual);
actual = exp.ToString();
Assert.AreEqual("Exp(x; λ = 0,342105263157895)", actual);
}
public void ConstructorTest2()
{
var exp = new ExponentialDistribution(rate: 0.42);
double mean = exp.Mean; // 2.3809523809523809
double median = exp.Median; // 1.6503504299046317
double var = exp.Variance; // 5.6689342403628125
double cdf = exp.DistributionFunction(x: 0.27); // 0.10720652870550407
double pdf = exp.ProbabilityDensityFunction(x: 0.27); // 0.3749732579436883
double lpdf = exp.LogProbabilityDensityFunction(x: 0.27); // -0.98090056770472311
double ccdf = exp.ComplementaryDistributionFunction(x: 0.27); // 0.89279347129449593
double icdf = exp.InverseDistributionFunction(p: cdf); // 0.27
double hf = exp.HazardFunction(x: 0.27); // 0.42
double chf = exp.CumulativeHazardFunction(x: 0.27); // 0.1134
string str = exp.ToString(CultureInfo.InvariantCulture); // Exp(x; λ = 0.42)
Assert.AreEqual(2.3809523809523809, mean);
Assert.AreEqual(1.6503504299046317, median);
Assert.AreEqual(5.6689342403628125, var);
Assert.AreEqual(0.1134, chf);
Assert.AreEqual(0.10720652870550407, cdf);
Assert.AreEqual(0.3749732579436883, pdf);
Assert.AreEqual(-0.98090056770472311, lpdf);
Assert.AreEqual(0.42, hf);
Assert.AreEqual(0.89279347129449593, ccdf);
Assert.AreEqual(0.27, icdf);
Assert.AreEqual("Exp(x; λ = 0.42)", str);
}
public void MultivariateMoments()
{
// create a random sample
MultivariateSample M = new MultivariateSample(3);
ContinuousDistribution d0 = new NormalDistribution();
ContinuousDistribution d1 = new ExponentialDistribution();
ContinuousDistribution d2 = new UniformDistribution();
Random rng = new Random(1);
int n = 10;
for (int i = 0; i < n; i++)
{
M.Add(d0.GetRandomValue(rng), d1.GetRandomValue(rng), d2.GetRandomValue(rng));
}
// test that moments agree
for (int i = 0; i < 3; i++)
{
int[] p = new int[3];
p[i] = 1;
Assert.IsTrue(TestUtilities.IsNearlyEqual(M.Column(i).Mean, M.RawMoment(p)));
p[i] = 2;
Assert.IsTrue(TestUtilities.IsNearlyEqual(M.Column(i).Variance, M.CentralMoment(p)));
for (int j = 0; j < i; j++)
{
int[] q = new int[3];
q[i] = 1;
q[j] = 1;
Assert.IsTrue(TestUtilities.IsNearlyEqual(M.TwoColumns(i, j).Covariance, M.CentralMoment(q)));
}
}
}
public void ConstructorTest2()
{
var exp = new ExponentialDistribution(rate: 0.42);
double mean = exp.Mean; // 2.3809523809523809
double median = exp.Median; // 1.6503504299046317
double var = exp.Variance; // 5.6689342403628125
double cdf = exp.DistributionFunction(x: 0.27); // 0.10720652870550407
double pdf = exp.ProbabilityDensityFunction(x: 0.27); // 0.3749732579436883
double lpdf = exp.LogProbabilityDensityFunction(x: 0.27); // -0.98090056770472311
double ccdf = exp.ComplementaryDistributionFunction(x: 0.27); // 0.89279347129449593
double icdf = exp.InverseDistributionFunction(p: cdf); // 0.27
double hf = exp.HazardFunction(x: 0.27); // 0.42
double chf = exp.CumulativeHazardFunction(x: 0.27); // 0.1134
string str = exp.ToString(CultureInfo.InvariantCulture); // Exp(x; λ = 0.42)
Assert.AreEqual(2.3809523809523809, mean);
Assert.AreEqual(1.6503504299046317, median);
Assert.AreEqual(5.6689342403628125, var);
Assert.AreEqual(0.1134, chf);
Assert.AreEqual(0.10720652870550407, cdf);
Assert.AreEqual(0.3749732579436883, pdf);
Assert.AreEqual(-0.98090056770472311, lpdf);
Assert.AreEqual(0.42, hf);
Assert.AreEqual(0.89279347129449593, ccdf);
Assert.AreEqual(0.27, icdf);
Assert.AreEqual("Exp(x; λ = 0.42)", str);
}
public void WilcoxonNullDistribution()
{
// Pick a very non-normal distribution
ContinuousDistribution d = new ExponentialDistribution();
Random rng = new Random(271828);
foreach (int n in TestUtilities.GenerateIntegerValues(4, 64, 4))
{
Sample wSample = new Sample();
ContinuousDistribution wDistribution = null;
for (int i = 0; i < 128; i++)
{
BivariateSample sample = new BivariateSample();
for (int j = 0; j < n; j++)
{
double x = d.GetRandomValue(rng);
double y = d.GetRandomValue(rng);
sample.Add(x, y);
}
TestResult wilcoxon = sample.WilcoxonSignedRankTest();
wSample.Add(wilcoxon.Statistic);
wDistribution = wilcoxon.Distribution;
}
TestResult ks = wSample.KolmogorovSmirnovTest(wDistribution);
Assert.IsTrue(ks.Probability > 0.05);
Assert.IsTrue(wSample.PopulationMean.ConfidenceInterval(0.99).ClosedContains(wDistribution.Mean));
Assert.IsTrue(wSample.PopulationStandardDeviation.ConfidenceInterval(0.99).ClosedContains(wDistribution.StandardDeviation));
}
}
public void KendallNullDistributionTest()
{
// Pick independent distributions for x and y, which needn't be normal and needn't be related.
ContinuousDistribution xDistrubtion = new LogisticDistribution();
ContinuousDistribution yDistribution = new ExponentialDistribution();
Random rng = new Random(314159265);
// generate bivariate samples of various sizes
foreach (int n in TestUtilities.GenerateIntegerValues(8, 64, 4))
{
Sample testStatistics = new Sample();
ContinuousDistribution testDistribution = null;
for (int i = 0; i < 128; i++)
{
BivariateSample sample = new BivariateSample();
for (int j = 0; j < n; j++)
{
sample.Add(xDistrubtion.GetRandomValue(rng), yDistribution.GetRandomValue(rng));
}
TestResult result = sample.KendallTauTest();
testStatistics.Add(result.Statistic);
testDistribution = result.Distribution;
}
TestResult r2 = testStatistics.KolmogorovSmirnovTest(testDistribution);
Assert.IsTrue(r2.RightProbability > 0.05);
Assert.IsTrue(testStatistics.PopulationMean.ConfidenceInterval(0.99).ClosedContains(testDistribution.Mean));
Assert.IsTrue(testStatistics.PopulationVariance.ConfidenceInterval(0.99).ClosedContains(testDistribution.Variance));
}
}
public void GammaFromExponential()
{
// test that x_1 + x_2 + ... + x_n ~ Gamma(n) when z ~ Exponential()
Random rng = new Random(1);
ExponentialDistribution eDistribution = new ExponentialDistribution();
// pick some low values of n so distribution is not simply normal
foreach (int n in new int[] { 2, 3, 4, 5 })
{
Sample gSample = new Sample();
for (int i = 0; i < 100; i++)
{
double sum = 0.0;
for (int j = 0; j < n; j++)
{
sum += eDistribution.GetRandomValue(rng);
}
gSample.Add(sum);
}
GammaDistribution gDistribution = new GammaDistribution(n);
TestResult result = gSample.KolmogorovSmirnovTest(gDistribution);
Assert.IsTrue(result.Probability > 0.05);
}
}
public void ExponentialDistributionChiSquareTest(double lambda)
{
var expDistribution = new ExponentialDistribution(lambda);
var test = ChiSquareTest.Test(expDistribution);
Assert.IsTrue(test);
}
public void ConstructorTest()
{
ExponentialDistribution n = new ExponentialDistribution(3.42521);
Assert.AreEqual(3.42521, n.Rate);
Assert.AreEqual(0.29195290215782393, n.Mean);
Assert.AreEqual(0.085236497078375897, n.Variance);
}
/// <summary>
/// Metodo que calcula la frecuencia esperada en un intervalo
/// </summary>
/// <param name="intervalo"></param>
/// <param name="tamanioMuestra"></param>
/// <param name="cantidadIntervalos"></param>
/// <returns></returns>
public override double CalcularFrecuenciaEsperadaEnIntervalo(Intervalo intervalo, int tamanioMuestra, int cantidadIntervalos)
{
var distribucion = new ExponentialDistribution(1 / Lambda);
var probabilidadEsperada = distribucion.LeftProbability(intervalo.LimiteSuperior) -
distribucion.LeftProbability(intervalo.LimiteInferior);
return(probabilidadEsperada * tamanioMuestra);
}
static void Main(string[] args)
{
const int maxItemsInQueue = 6;
int ia = 30, ib = 10;
var s = new Schema(maxItemsInQueue, 2);
const int totalWorkingTime = 10000;
var fr = new ExponentialDistribution(7, 5, 3, uint.MaxValue, totalWorkingTime, ia);
var frl = fr.Generate();
var dr = new ExponentialDistribution(7, 5, 3, uint.MaxValue, totalWorkingTime, ib);
var drl = dr.Generate();
int sumOch = 0;
int wokingWorker = 0;
for (int i = 0; i < totalWorkingTime; i++)
{
if (!s.HasGeneratedItem(i))
{
s.GenerateItem(i + (int)(frl.Skip(r.Next(totalWorkingTime)).First() * ia));
}
if (s.IsGenerated(i))
{
s.AddToQueue();
}
for (int j = 0; j < s.CountInQueue; j++)
{
if (!s.AllChanelsInWorkingState(i))
{
var endTime = i + (int)(drl.Skip(r.Next(totalWorkingTime)).First() * ib);
if (endTime < totalWorkingTime)
{
s.AddToWork(i, endTime);
s.RemoveFromQueue();
}
}
}
wokingWorker += s.CountWorkingWorkers(i);
sumOch += s.CountInQueue;
}
Console.WriteLine("Абсолютная пропускная способность = " + 1 / (double)ia * (1 - s.CountDroped / (double)totalWorkingTime));
Console.WriteLine("Среднее число занятых рабочих = " + wokingWorker / (double)totalWorkingTime);
Console.WriteLine("Среднее число неисправных станков = " + sumOch / (double)totalWorkingTime);
Console.ReadKey();
}
public void DifferenceOfTwoExponentialDistributionsChiSquareTest(double lambda)
{
var distr1 = new ExponentialDistribution(lambda);
var distr2 = new ExponentialDistribution(lambda);
var diff = distr1 - distr2;
var test = ChiSquareTest.Test(diff);
Assert.IsTrue(test);
}
public void QuotientOfTwoExponentialDistributionsChiSquareTest(double lambda)
{
var distr1 = new ExponentialDistribution(lambda);
var distr2 = new ExponentialDistribution(lambda);
var quotient = distr1 / distr2;
var test = ChiSquareTest.Test(quotient);
Assert.IsTrue(test);
}
public ExponentialEstimate(List <double> data) : base(data)
{
if (data.Min() <= 0)
{
this.score = -1; //negative data implies it can't be Exponential
return;
}
this.DistributionType = Distribution.Exponential;
testDistribution = new ExponentialDistribution(data.Average());
this.RunTestOfFit();
}
public void SumOfTwoExponentialDistributionsChiSquareTest(double lambda)
{
var distr1 = new ExponentialDistribution(lambda);
var distr2 = new ExponentialDistribution(lambda);
var sum = distr1 + distr2;
var test = ChiSquareTest.Test(sum);
Assert.IsTrue(test);
}
public void ProductOfTwoExponentialDistributionsChiSquareTest(double lambda)
{
var distr1 = new ExponentialDistribution(lambda);
var distr2 = new ExponentialDistribution(lambda);
var product = distr1 * distr2;
var test = ChiSquareTest.Test(product);
Assert.IsTrue(test);
}
public void ExponentialDistributionGeneratorTest(double lambda,
double distributionEpsilon, double statEpsilon, int totalValues, int totalIntervals)
{
var dist = new ExponentialDistribution(lambda);
var generator = new ExponentialDistributionGenerator(dist);
CheckAssert(
generator, dist,
distributionEpsilon, statEpsilon,
totalValues, totalIntervals);
}
public void GenerateTest()
{
ExponentialDistribution target = new ExponentialDistribution(2.5);
double[] samples = target.Generate(1000000);
var actual = ExponentialDistribution.Estimate(samples);
actual.Fit(samples);
Assert.AreEqual(2.5, actual.Rate, 0.01);
}
public List <double> getFrecuenciasEsperadas(List <Intervalo> intervalos)
{
var _frecuencias = new List <double>(intervalos.Count);
ContinuousDistribution d = new ExponentialDistribution(1 / _lambda);
foreach (var _intervalo in intervalos)
{
var _frecuencia = d.LeftProbability(_intervalo._fin) - d.LeftProbability(_intervalo._inicio);
_frecuencias.Add(_frecuencia);
}
return(_frecuencias);
}
private UnivariateContinuousDistribution FitDistribution(double [] samples)
{
if (samples.Length < 10)
{
return(new EmpiricalDistribution(samples));
}
else
{
var exp = new ExponentialDistribution(); // new LognormalDistribution(); //
exp.Fit(samples);
return(exp);
}
}
public void WilcoxonNullDistribution()
{
// Pick a very non-normal distribution
ContinuousDistribution d = new ExponentialDistribution();
Random rng = new Random(271828);
foreach (int n in TestUtilities.GenerateIntegerValues(4, 64, 4))
{
Sample wContinuousSample = new Sample();
ContinuousDistribution wContinuousDistribution = null;
List <int> wDiscreteSample = new List <int>();
DiscreteDistribution wDiscreteDistribution = null;
for (int i = 0; i < 256; i++)
{
BivariateSample sample = new BivariateSample();
for (int j = 0; j < n; j++)
{
double x = d.GetRandomValue(rng);
double y = d.GetRandomValue(rng);
sample.Add(x, y);
}
TestResult wilcoxon = sample.WilcoxonSignedRankTest();
if (wilcoxon.UnderlyingStatistic != null)
{
wDiscreteSample.Add(wilcoxon.UnderlyingStatistic.Value);
wDiscreteDistribution = wilcoxon.UnderlyingStatistic.Distribution;
}
else
{
wContinuousSample.Add(wilcoxon.Statistic.Value);
wContinuousDistribution = wilcoxon.Statistic.Distribution;
}
}
if (wDiscreteDistribution != null)
{
TestResult chi2 = wDiscreteSample.ChiSquaredTest(wDiscreteDistribution);
Assert.IsTrue(chi2.Probability > 0.01);
}
else
{
TestResult ks = wContinuousSample.KolmogorovSmirnovTest(wContinuousDistribution);
Assert.IsTrue(ks.Probability > 0.01);
Assert.IsTrue(wContinuousSample.PopulationMean.ConfidenceInterval(0.99).ClosedContains(wContinuousDistribution.Mean));
Assert.IsTrue(wContinuousSample.PopulationStandardDeviation.ConfidenceInterval(0.99).ClosedContains(wContinuousDistribution.StandardDeviation));
}
}
}
public void Issue_632()
{
// https://github.com/accord-net/framework/issues/632
var n = new ExponentialDistribution(4.2);
Assert.AreEqual(0, n.DistributionFunction(-10));
Assert.AreEqual(0, n.ProbabilityDensityFunction(-10));
Assert.AreEqual(System.Math.Log(0), n.LogProbabilityDensityFunction(-10));
Assert.AreEqual(0, n.DistributionFunction(0));
Assert.AreEqual(4.2, n.ProbabilityDensityFunction(0));
Assert.AreEqual(System.Math.Log(4.2), n.LogProbabilityDensityFunction(0));
}
public FuncionExponencial(double[] eventos) : base(eventos)
{
try
{
DistribucionContinua = new ExponentialDistribution();
DistribucionContinua.Fit(eventos);
this.L = ((ExponentialDistribution)DistribucionContinua).Rate.ToString("0.0000");
Resultado = new ResultadoAjuste(StringFDP, StringInversa, DistribucionContinua.StandardDeviation, DistribucionContinua.Mean, DistribucionContinua.Variance, this);
}
catch (Exception)
{
Resultado = null;
}
}
public void MixtureExponentialTest1()
{
var exp = new ExponentialDistribution();
var mixture = new MixtureDistribution(exp, new ShiftedDistribution(exp, 10));
var comparer = new AbsoluteEqualityComparer(1e-4);
for (double p = 0; p < 1.0; p += 0.01)
{
double q = mixture.Quantile(p);
output.WriteLine($"{p.ToStringInvariant("N2")}: {q.ToStringInvariant()}");
Assert.Equal(p, mixture.Cdf(q), comparer);
}
}
public void ConstructorTest2()
{
var exp = new ExponentialDistribution(rate: 0.42);
double mean = exp.Mean; // 2.3809523809523809
double median = exp.Median; // 1.6503504299046317
double var = exp.Variance; // 5.6689342403628125
double mode = exp.Mode; // 0.0
double cdf = exp.DistributionFunction(x: 0.27); // 0.10720652870550407
double pdf = exp.ProbabilityDensityFunction(x: 0.27); // 0.3749732579436883
double lpdf = exp.LogProbabilityDensityFunction(x: 0.27); // -0.98090056770472311
double ccdf = exp.ComplementaryDistributionFunction(x: 0.27); // 0.89279347129449593
double icdf = exp.InverseDistributionFunction(p: cdf); // 0.27
double hf = exp.HazardFunction(x: 0.27); // 0.42
double chf = exp.CumulativeHazardFunction(x: 0.27); // 0.1134
string str = exp.ToString(CultureInfo.InvariantCulture); // Exp(x; λ = 0.42)
Assert.AreEqual(2.3809523809523809, mean);
Assert.AreEqual(1.6503504299046317, median);
Assert.AreEqual(0.0, mode);
Assert.AreEqual(5.6689342403628125, var);
Assert.AreEqual(0.1134, chf);
Assert.AreEqual(0.10720652870550407, cdf);
Assert.AreEqual(0.3749732579436883, pdf);
Assert.AreEqual(-0.98090056770472311, lpdf);
Assert.AreEqual(0.42, hf);
Assert.AreEqual(0.89279347129449593, ccdf);
Assert.AreEqual(0.27, icdf);
Assert.AreEqual("Exp(x; λ = 0.42)", str);
var range1 = exp.GetRange(0.95);
var range2 = exp.GetRange(0.99);
var range3 = exp.GetRange(0.01);
Assert.AreEqual(0.12212689139892995, range1.Min);
Assert.AreEqual(7.1326958894142622, range1.Max);
Assert.AreEqual(0.023929371079765359, range2.Min);
Assert.AreEqual(10.964690919019265, range2.Max);
Assert.AreEqual(0.023929371079765359, range3.Min);
Assert.AreEqual(10.964690919019265, range3.Max);
}
/// <summary>
/// Updates the given chart with the specified distribution.
/// </summary>
/// <param name="chart">A chart.</param>
/// <param name="dist">The distribution.</param>
/// <param name="function">The distribution function to plot.</param>
/// <param name="numInterpolatedValues">The number of interpolated values.</param>
/// <returns>A new chart.</returns>
/// <remarks>
/// Plots the specified function of the given distribution for 0.0001 <= p <= 0.9999.
/// <br/>
/// Titles are added only if chart does not currently contain any titles.
/// <br/>
/// chart.Series[0] is replaced, or added if necessary.
/// </remarks>
public static void Update( ref ChartControl chart, ExponentialDistribution dist, DistributionFunction function = DistributionFunction.PDF, int numInterpolatedValues = 100 )
{
List<string> titles = new List<string>()
{
"ExponentialDistribution",
String.Format("\u03BB={0}", dist.Lambda)
};
UpdateContinuousDistribution( ref chart, dist, titles, function, numInterpolatedValues );
}
public void GenerateTest()
{
ExponentialDistribution target = new ExponentialDistribution(2.5);
double[] samples = target.Generate(1000000);
var actual = ExponentialDistribution.Estimate(samples);
actual.Fit(samples);
Assert.AreEqual(2.5, actual.Rate, 0.01);
}
public void MedianTest()
{
ExponentialDistribution target = new ExponentialDistribution(2.5);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
}
public void FitTest1()
{
double[] values =
{
0, 1, 2, 4, 2, 3, 5, 7, 4, 3, 2, 1, 4,
};
var exp = new ExponentialDistribution();
exp.Fit(values);
string actual;
Thread.CurrentThread.CurrentCulture = CultureInfo.GetCultureInfo("fr-FR");
actual = exp.ToString("G3", CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342)", actual);
actual = exp.ToString("G3");
Assert.AreEqual("Exp(x; λ = 0,342)", actual);
actual = exp.ToString(CultureInfo.InvariantCulture);
Assert.AreEqual("Exp(x; λ = 0.342105263157895)", actual);
actual = exp.ToString();
Assert.AreEqual("Exp(x; λ = 0,342105263157895)", actual);
}
public void GenerateTest2()
{
ExponentialDistribution target = new ExponentialDistribution(2.5);
double[] samples = new double[1000000];
for (int i = 0; i < samples.Length; i++)
samples[i] = target.Generate();
var actual = ExponentialDistribution.Estimate(samples);
actual.Fit(samples);
Assert.AreEqual(2.5, actual.Rate, 0.01);
}
/// <summary>
/// Shows a new chart in a default form.
/// </summary>
/// <param name="dist">The distribution.</param>
/// <param name="function">The distribution function to plot.</param>
/// <param name="numInterpolatedValues">The number of interpolated values.</param>
/// <remarks>
/// Equivalent to:
/// <code>
/// NMathStatsChart.Show( ToChart( dist, function, numInterpolatedValues ) );
/// </code>
/// </remarks>
public static void Show( ExponentialDistribution dist, DistributionFunction function = DistributionFunction.PDF, int numInterpolatedValues = 100 )
{
Show( ToChart( dist, function, numInterpolatedValues ) );
}
public PoissonInterarrivalTimer(double offeredLoad)
{
this.exponentialDistribution = new ExponentialDistribution(new Random(), offeredLoad);
this.lastEventTime = DateTime.UtcNow;
}
public void CumulativeDistributionTest()
{
ExponentialDistribution n = new ExponentialDistribution(3);
double[] expected = { 0, 0.950213, 0.997521, 0.999877, 0.999994, 1.0 };
double[] actual = new double[expected.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = n.DistributionFunction(i);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-6);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
//---------------------------------------------------------------------
///<summary>
/// Run the Biomass Insects extension at a particular timestep.
///</summary>
public override void Run()
{
running = true;
UI.WriteLine(" Processing landscape for Biomass Insect events ...");
foreach(IInsect insect in manyInsect)
{
if(insect.MortalityYear == Model.Core.CurrentTime)
Outbreak.Mortality(insect);
// Copy the data from current to last
foreach (ActiveSite site in Model.Core.Landscape)
insect.LastYearDefoliation[site] = insect.ThisYearDefoliation[site];
insect.ThisYearDefoliation.ActiveSiteValues = 0.0;
//SiteVars.BiomassRemoved.ActiveSiteValues = 0;
insect.ActiveOutbreak = false;
insect.SingleOutbreakYear = false;
NormalDistribution randVar = new NormalDistribution(RandomNumberGenerator.Singleton);
randVar.Mu = 0; // mean
randVar.Sigma = 1; // std dev
double randomNum = randVar.NextDouble();
ExponentialDistribution randVarE = new ExponentialDistribution(RandomNumberGenerator.Singleton);
randVarE.Lambda = insect.MeanDuration; // rate
double randomNumE = randVarE.NextDouble();
// First, has enough time passed since the last outbreak?
double timeBetweenOutbreaks = insect.MeanTimeBetweenOutbreaks + (insect.StdDevTimeBetweenOutbreaks * randomNum);
//double duration = insect.MeanDuration + (insect.StdDevDuration * randomNum);
double duration = Math.Round(randomNumE + 1);
if (duration > 5) // Limit maximum outbreak duration to 5 years for now.
duration = duration - 3;
double timeAfterDuration = timeBetweenOutbreaks - duration;
//UI.WriteLine("Calculated time between = {0}. inputMeanTime={1}, inputStdTime={2}.", timeBetweenOutbreaks, insect.MeanTimeBetweenOutbreaks, insect.StdDevTimeBetweenOutbreaks);
//UI.WriteLine("Calculated duration = {0}. inputMeanDura={1}, inputStdDura={2}.", duration, insect.MeanDuration, insect.StdDevDuration);
//UI.WriteLine("Insect Start Time = {0}, Stop Time = {1}.", insect.OutbreakStartYear, insect.OutbreakStopYear);
if(Model.Core.CurrentTime == 1)
{
//UI.WriteLine(" Year 1: Setting initial start and stop times.");
insect.OutbreakStartYear = (int) (timeBetweenOutbreaks / 2.0) + 1;
insect.OutbreakStopYear = insect.OutbreakStartYear + (int) duration - 1;
UI.WriteLine(" {0} is not active. StartYear={1}, StopYear={2}, CurrentYear={3}.", insect.Name, insect.OutbreakStartYear, insect.OutbreakStopYear, Model.Core.CurrentTime);
}
else if(insect.OutbreakStartYear <= Model.Core.CurrentTime
&& insect.OutbreakStopYear > Model.Core.CurrentTime)
{
//UI.WriteLine(" An outbreak starts or continues. Start and stop time do not change.");
insect.ActiveOutbreak = true;
UI.WriteLine(" {0} is active. StartYear={1}, StopYear={2}, CurrentYear={3}.", insect.Name, insect.OutbreakStartYear, insect.OutbreakStopYear, Model.Core.CurrentTime);
insect.MortalityYear = Model.Core.CurrentTime + 1;
}
//Special case for single year outbreak.
else if(insect.OutbreakStartYear <= Model.Core.CurrentTime
&& insect.OutbreakStopYear <= Model.Core.CurrentTime)
{
insect.ActiveOutbreak = true;
UI.WriteLine(" {0} is active. StartYear={1}, StopYear={2}, CurrentYear={3}.", insect.Name, insect.OutbreakStartYear, insect.OutbreakStopYear, Model.Core.CurrentTime);
if (insect.OutbreakStartYear == insect.OutbreakStopYear)
insect.SingleOutbreakYear = true;
insect.MortalityYear = Model.Core.CurrentTime + 1;
insect.OutbreakStartYear = Model.Core.CurrentTime + (int)timeBetweenOutbreaks;
insect.OutbreakStopYear = insect.OutbreakStartYear + (int)duration - 1;
}
else if(insect.OutbreakStopYear <= Model.Core.CurrentTime
&& timeAfterDuration > Model.Core.CurrentTime - insect.OutbreakStopYear)
{
//UI.WriteLine(" In between outbreaks, reset start and stop times.");
insect.ActiveOutbreak = true;
UI.WriteLine(" {0} is active. StartYear={1}, StopYear={2}, CurrentYear={3}.", insect.Name, insect.OutbreakStartYear, insect.OutbreakStopYear, Model.Core.CurrentTime);
insect.MortalityYear = Model.Core.CurrentTime + 1;
insect.OutbreakStartYear = Model.Core.CurrentTime + (int) timeBetweenOutbreaks;
insect.OutbreakStopYear = insect.OutbreakStartYear + (int) duration - 1;
}
//UI.WriteLine(" Insect Start Time = {0}, Stop Time = {1}.", insect.OutbreakStartYear, insect.OutbreakStopYear);
if(insect.ActiveOutbreak)
{
//UI.WriteLine(" OutbreakStartYear={0}.", insect.OutbreakStartYear);
if(insect.OutbreakStartYear == Model.Core.CurrentTime || insect.SingleOutbreakYear)
// Initialize neighborhoodGrowthReduction with patches
Outbreak.InitializeDefoliationPatches(insect);
else
insect.NeighborhoodDefoliation.ActiveSiteValues = 0;
double sumDefoliation = 0.0;
int numSites = 0;
//foreach(ActiveSite site in Model.Core.Landscape)
//{
// sumDefoliation += insect.ThisYearDefoliation[site];
// if(insect.ThisYearDefoliation[site] > 0.0)
// numSites++;
//}
double meanDefoliation = sumDefoliation / (double) numSites;
log.Write("{0},{1},{2},{3},{4},{5}",
Model.Core.CurrentTime,
insect.Name,
insect.OutbreakStartYear,
insect.OutbreakStopYear,
sumDefoliation,
numSites
);
//foreach (IEcoregion ecoregion in Ecoregions.Dataset)
// log.Write(",{0}", 1);
log.WriteLine("");
}
//Only write maps if an outbreak is active.
//if (!insect.ActiveOutbreak)
//if (insect.OutbreakStartYear <= Model.Core.CurrentTime
// && insect.OutbreakStopYear + 1 >= Model.Core.CurrentTime)
//if (insect.OutbreakStartYear <= Model.Core.CurrentTime)
// | insect.MortalityYear = Model.Core.CurrentTime)
// continue;
//----- Write Insect GrowthReduction maps --------
IOutputRaster<UShortPixel> map = CreateMap((Model.Core.CurrentTime - 1), insect.Name);
using (map) {
UShortPixel pixel = new UShortPixel();
foreach (Site site in Model.Core.Landscape.AllSites) {
if (site.IsActive)
pixel.Band0 = (ushort) (insect.LastYearDefoliation[site] * 100.0);
else
// Inactive site
pixel.Band0 = 0;
map.WritePixel(pixel);
}
}
//----- Write Initial Patch maps --------
//IOutputRaster<UShortPixel> map2 = CreateMap(Model.Core.CurrentTime, ("InitialPatchMap" + insect.Name));
//using (map2) {
// UShortPixel pixel = new UShortPixel();
// foreach (Site site in Model.Core.Landscape.AllSites) {
// if (site.IsActive)
// {
// if (insect.Disturbed[site])
// pixel.Band0 = (ushort) (SiteVars.InitialOutbreakProb[site] * 100);
// else
// pixel.Band0 = 0;
// }
// else
// {
// // Inactive site
// pixel.Band0 = 0;
// }
// map2.WritePixel(pixel);
// }
//}
//----- Write Biomass Reduction maps --------
IOutputRaster<UShortPixel> map3 = CreateMap(Model.Core.CurrentTime, ("BiomassRemoved" + insect.Name));
using (map3) {
UShortPixel pixel = new UShortPixel();
foreach (Site site in Model.Core.Landscape.AllSites) {
if (site.IsActive)
{
// TESTING added by RMS
if(SiteVars.BiomassRemoved[site] > 0)
// UI.WriteLine(" Biomass revoved at {0}/{1}: {2}.", site.Location.Row, site.Location.Column, SiteVars.BiomassRemoved[site]);
pixel.Band0 = (ushort) (SiteVars.BiomassRemoved[site] / 100);
else
pixel.Band0 = 0;
}
else
{
// Inactive site
pixel.Band0 = 0;
}
map3.WritePixel(pixel);
//Zero out the BiomassRemoved after the last insect mortality event in a given year.
//if (SiteVars.BiomassRemoved[site] > 0 && SiteVars.TimeOfLastEvent[site] < Model.Core.CurrentTime)
SiteVars.BiomassRemoved[site] = 0;
}
}
}
}
public void ProbabilityDistributionTest()
{
ExponentialDistribution n = new ExponentialDistribution(3);
double[] expected = { 3, 0.149361, 0.00743626, 0.000370229, 0.0000184326 };
double[] actual = new double[expected.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = n.ProbabilityDensityFunction(i);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
/// <summary>
/// Returns a new line chart plotting the specified function of the given distribution for 0.0001 <= p <= 0.9999.
/// </summary>
/// <param name="dist">The distribution.</param>
/// <param name="function">The distribution function to plot.</param>
/// <param name="numInterpolatedValues">The number of interpolated values.</param>
/// <returns>A new chart.</returns>
public static ChartControl ToChart( ExponentialDistribution dist, DistributionFunction function = DistributionFunction.PDF, int numInterpolatedValues = 100 )
{
ChartControl chart = GetDefaultChart();
Update( ref chart, dist, function, numInterpolatedValues );
return chart;
}
public void ProbabilityDistributionTest2()
{
ExponentialDistribution n = new ExponentialDistribution(0.42);
double[] expected = { 0.42, 0.27596, 0.181318, 0.119135, 0.0782771, 0.0514317 };
double[] actual = new double[expected.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = n.ProbabilityDensityFunction(i);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}
