public static IEnumerable <T> Samples <T>(this IDistribution <T> d)
{
while (true)
{
yield return(d.Sample());
}
}
/// <summary>
/// Infinite enumerable of samples from this <see cref="IDistribution{T}"/>.
/// </summary>
/// <typeparam name="T">Type of items in this <see cref="IDistribution{T}"/></typeparam>
/// <param name="distribution">The distribution we're sampling.</param>
/// <param name="random"></param>
/// <returns>The infinite <see cref="IEnumerable{T}"/> of samples.</returns>
public static IEnumerable <T> Samples <T>(this IDistribution <T> distribution, IRNG random)
{
while (true)
{
yield return(distribution.Sample(random));
}
}
public static IEnumerable <T> TakeSamples <T>(this IDistribution <T> distribution, int samples)
{
for (int i = 0; i < samples; i++)
{
yield return(distribution.Sample());
}
}
public void SingleAverage(IDistribution <Single> dist, UInt64 seed)
{
const Int32 iterations = 10_000;
var rng = Pcg32.Create(seed, 11634580027462260723ul);
Double mean = 0;
for (var i = 0; i < iterations; i++)
{
var result = dist.Sample(rng);
var delta = result - mean;
mean += delta / (i + 1);
Assert.True(0 <= result);
Assert.True(result <= 1);
}
Assert.True(Statistics.WithinConfidence(popMean: 0.5, popStdDev: 0.5, mean, iterations));
Double mean2 = 0;
for (var i = 0; i < iterations; i++)
{
Assert.True(dist.TrySample(rng, out var result));
var delta = result - mean2;
mean2 += delta / (i + 1);
Assert.True(0 <= result);
Assert.True(result <= 1);
}
Assert.True(Statistics.WithinConfidence(popMean: 0.5, popStdDev: 0.5, mean2, iterations));
}
public static IEnumerable <T> Samples <T>([NotNull] this IDistribution <T> d)
{
while (true)
{
yield return(d.Sample());
}
// ReSharper disable once IteratorNeverReturns
}
public void Init(LocalRandom random, int startYear, int numYears)
{
this.startYear = startYear;
values = new double[numYears];
for (var i = 0; i < numYears; ++i)
{
values[i] = pdf.Sample(random);
}
}
public double Evaluate(Vector input)
{
return(Function.Evaluate(input) * (Offset.Parameter + Scale.Parameter * Math.Abs(noise.Sample())));
}
public int Sample()
{
return(rows.Sample().Sample());
}
public TResult Sample <TRng>(TRng rng) where TRng : notnull, IRng
{
var sample = _distribution.Sample(rng);
return(_selector(sample));
}