public void CanSampleSequence()
{
var n = new Triangular(0.1, 0.3, 0.2);
var ied = n.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
public double[] getSamples(int num) // 获取指定个数的样本
{
double[] ret = new double[num];
int[] ret_int = new int[num];
switch (DistributionName)
{
case "Normal":
normalDis.Samples(ret);
break;
case "ContinuousUniform":
continuousUniformDis.Samples(ret);
break;
case "Triangular":
triangularDis.Samples(ret);
break;
case "StudentT":
studentTDis.Samples(ret);
break;
case "DiscreteUniform":
discreteUniform.Samples(ret_int);
for (int i = 0; i < num; i++)
{
ret[i] = ret_int[i];
}
break;
}
return(ret);
}
public void CanSampleSequenceStatic()
{
var ied = Triangular.Samples(new Random(0), 2.0, 3.0, 2.5);
GC.KeepAlive(ied.Take(5).ToArray());
ied = Triangular.Samples(10.0, 100.0, 30.0);
GC.KeepAlive(ied.Take(5).ToArray());
}
public static double[] triangular(double v1, double v2, double v3, int num)
{
var t = new Triangular(v1, v2, v3);
double[] ret = new double[num];
t.Samples(ret);
return(ret);
}
public void Setup()
{
// Debug
// Random rnd = new Random(0); // for repetability
// Triangular.Samples(rnd, damageJitter, 0.9, 1.1, 1.0);
Triangular.Samples(damageJitter, 0.9, 1.1, 1.0);
for (int i = 5000; i < 25000; i += 2500)
{
multipliersLarge.Add(i);
}
for (int i = 500; i < 3500; i += 500)
{
multipliers.Add(i);
}
}
public void FailSampleSequenceStatic()
{
Assert.That(() => Triangular.Samples(new Random(0), 1.0, -1.0, 0.1).First(), Throws.ArgumentException);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="https://en.wikipedia.org/wiki/Triangular_distribution">Triangular distribution</a>
public void Run()
{
// 1. Initialize
var triangular = new Triangular(0, 1, 0.3);
Console.WriteLine(@"1. Initialize the new instance of the Triangular distribution class with parameters Lower = {0}, Upper = {1}, Mode = {2}", triangular.LowerBound, triangular.UpperBound, triangular.Mode);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", triangular);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", triangular.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", triangular.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", triangular.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", triangular.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", triangular.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", triangular.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", triangular.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", triangular.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", triangular.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", triangular.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", triangular.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", triangular.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 10 samples
Console.WriteLine(@"3. Generate 10 samples of the Triangular distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(triangular.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 10000 samples with starting parameters
Console.WriteLine(@"4. Generate 100000 samples of the Triangular(0, 1, 0.3) distribution and display histogram");
var data = new double[100000];
Triangular.Samples(data, 0.0, 1.0, 0.3);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 10000 with different parameters
Console.WriteLine(@"4. Generate 100000 samples of the Triangular(2, 10, 8) distribution and display histogram");
Triangular.Samples(data, 2.0, 10.0, 8.0);
ConsoleHelper.DisplayHistogram(data);
}
public double[] GetSampleData(string distType, double mostLikelyEstimate,
double lowEstimate, double highEstimate)
{
if (Iterations > 10000)
{
Iterations = 10000;
}
if (Iterations <= 2)
{
Iterations = 1000;
}
if (this.CILevel < 10)
{
this.CILevel = 90;
}
if (this.CILevel > 99)
{
this.CILevel = 99;
}
Random rnd = new Random(Random);
mostLikelyEstimate = Math.Round(mostLikelyEstimate, 4);
lowEstimate = Math.Round(lowEstimate, 4);
highEstimate = Math.Round(highEstimate, 4);
var sampledata = new double[Iterations];
if (distType == Calculator1.RUC_TYPES.triangle.ToString())
{
if (lowEstimate >= mostLikelyEstimate || lowEstimate == 0)
{
//arbitrary rules (25%)
lowEstimate = mostLikelyEstimate * .75;
//no errors: lowEstimate = 0 is often the case
//sb.AppendLine(Errors.GetMessage("DATA_BADDISTRIBUTION"));
}
if (highEstimate <= mostLikelyEstimate || highEstimate == 0)
{
//arbitrary rules (25%)
highEstimate = mostLikelyEstimate * 1.25;
}
if (Random != 0)
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(rnd, sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
else
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.normal.ToString())
{
//generate samples of the Normal(mean, sd) distribution;
if (Random != 0)
{
Normal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Normal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.lognormal.ToString())
{
if (Random != 0)
{
LogNormal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
LogNormal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.weibull.ToString())
{
if (Random != 0)
{
Weibull.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Weibull.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.beta.ToString())
{
if (Random != 0)
{
Beta.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Beta.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.pareto.ToString())
{
if (Random != 0)
{
Pareto.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Pareto.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.uniform.ToString())
{
var sampleints = new int[Iterations];
int iLower = CalculatorHelpers.ConvertStringToInt(lowEstimate.ToString());
int iUpper = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
DiscreteUniform.Samples(rnd, sampleints, iLower, iUpper);
}
else
{
DiscreteUniform.Samples(sampleints, iLower, iUpper);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.bernoulli.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Bernoulli.Samples(rnd, sampleints, lowEstimate);
}
else
{
Bernoulli.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.poisson.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Poisson.Samples(rnd, sampleints, lowEstimate);
}
else
{
Poisson.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.binomial.ToString())
{
var sampleints = new int[Iterations];
int iUpperEstimate = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
Binomial.Samples(rnd, sampleints, lowEstimate, iUpperEstimate);
}
else
{
Binomial.Samples(sampleints, lowEstimate, iUpperEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.gamma.ToString())
{
//generate samples of the Gamma(shape, scale) distribution;
if (Random != 0)
{
Gamma.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Gamma.Samples(sampledata, lowEstimate, highEstimate);
}
}
else
{
//don't force them to use distribution
}
//hold for possible infernet use
//else if (distType == Calculator1.RUC_TYPES.dirichlet.ToString())
//{
// //generate samples of the Dirichlet(random, alpha) distribution;
// Dirichlet.Sample(sampledata, lowEstimate);
//}
//else if (distType == Calculator1.RUC_TYPES.wishart.ToString())
//{
// //generate samples of the Wishart(random, degrees of freedom, scale) distribution;
// Wishart.Sample(sampledata, lowEstimate, highEstimate);
//}
//the mathlibrary supports more than a dozen additional distributions
return(sampledata);
}