public void CanSampleSequence()
{
var n = new LogNormal(1.0, 2.0);
var ied = n.Samples();
ied.Take(5).ToArray();
}
public void CanEstimateParameters(double mu, double sigma)
{
var original = new LogNormal(mu, sigma, new Random(100));
var estimated = LogNormal.Estimate(original.Samples().Take(10000));
AssertHelpers.AlmostEqual(mu, estimated.Mu, 2);
AssertHelpers.AlmostEqual(sigma, estimated.Sigma, 2);
}
public PortfolioPath(int yearsUntilRetirement, double expectedReturn, double variance, double initialPortfolioValue, double annualContribution, double incomeDraw, int yearsPlannedRetirement)
{
this.folioReturn = expectedReturn;
this.folioStDev = Math.Sqrt(variance) / 100;
this.annualContribution = annualContribution;
this.incomeDraw = incomeDraw;
this.initialPortfolioValue = initialPortfolioValue;
this.retirement = yearsUntilRetirement;
this.nSteps = retirement + yearsPlannedRetirement;
this.portfolioValueList = new List <decimal>();
this.portfolioValueList.Add((decimal)initialPortfolioValue);
LogNormal lognormal = LogNormal.WithMuSigma(folioReturn, folioStDev);
IEnumerable <double> returns = lognormal.Samples().Take(nSteps);
this.endingPortfolioValue = returns.Aggregate(initialPortfolioValue, ComputeNextPortfolioValue);
}
public void FailSampleSequenceStatic()
{
Assert.Throws <ArgumentOutOfRangeException>(() => { var ied = LogNormal.Samples(new Random(), 0.0, -1.0).First(); });
}
public void CanSampleSequenceStatic()
{
var ied = LogNormal.Samples(new Random(), 0.0, 1.0);
ied.Take(5).ToArray();
}
public void FailSampleSequenceStatic()
{
Assert.That(() => { var ied = LogNormal.Samples(new Random(0), 0.0, -1.0).First(); }, Throws.ArgumentException);
}
public void CanSampleSequenceStatic()
{
var ied = LogNormal.Samples(new Random(0), 0.0, 1.0);
GC.KeepAlive(ied.Take(5).ToArray());
}
public void FailSampleSequenceStatic()
{
var ied = LogNormal.Samples(new Random(), 0.0, -1.0).First();
}
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);
}
public void CanSampleSequence()
{
var n = new LogNormal(1.0, 2.0);
var ied = n.Samples();
var e = ied.Take(5).ToArray();
}
public void CanEstimateParameters(double mu, double sigma)
{
var original = new LogNormal(mu, sigma, new Random(100));
var estimated = LogNormal.Estimate(original.Samples().Take(10000));
AssertHelpers.AlmostEqual(mu, estimated.Mu, 2);
AssertHelpers.AlmostEqual(sigma, estimated.Sigma, 2);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">LogNormal distribution</a>
public void Run()
{
// 1. Initialize the new instance of the LogNormal distribution class with parameters Mu = 0, Sigma = 1
var logNormal = new LogNormal(0, 1);
Console.WriteLine(@"1. Initialize the new instance of the LogNormal distribution class with parameters Mu = {0}, Sigma = {1}", logNormal.Mu, logNormal.Sigma);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", logNormal);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", logNormal.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", logNormal.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", logNormal.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", logNormal.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", logNormal.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", logNormal.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", logNormal.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", logNormal.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", logNormal.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", logNormal.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", logNormal.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", logNormal.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples
Console.WriteLine(@"3. Generate 10 samples");
for (var i = 0; i < 10; i++)
{
Console.Write(logNormal.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram");
var data = new double[100000];
LogNormal.Samples(data, 0.0, 1.0);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram");
LogNormal.Samples(data, 0.0, 0.5);
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram");
LogNormal.Samples(data, 5.0, 0.25);
ConsoleHelper.DisplayHistogram(data);
}
