/// <summary>
/// Initializes a new instance of the <see cref="UniformRealSampler"/> class.
/// </summary>
/// <param name="generator">
/// The underlying bit generator to use.
/// </param>
/// <param name="min">
/// The lower bound of the values that will be generated.
/// </param>
/// <param name="max">
/// The upper bound of the values that will be generated.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <paramref name="generator"/> is null.
/// </exception>
/// <exception cref="ArgumentException">
/// If <paramref name="min"/> is infinite or nan.
/// If <paramref name="max"/> is infinite or nan.
/// If <paramref name="min"/> is greater than or equal to <paramref name="max"/>.
/// </exception>
public UniformRealSampler(IRandomNumberEngine generator, double min, double max)
{
UniformRealDistribution.ValidateParameters(min, max);
this.generator = generator ?? throw new ArgumentNullException(nameof(generator));
this.Min = min;
this.Max = max;
}
/// <summary>
/// Initializes a new instance of the <see cref="MultivariateNormalSampler"/> class.
/// </summary>
/// <param name="engine">
/// The simulation engine to be used for sampling.
/// </param>
/// <param name="mean">
/// The mean values of the variables to be generated simultaneously.
/// </param>
/// <param name="choleskyFactor">
/// The Cholesky factorization of a matrix defining the desired joint distribution.
/// </param>
private MultivariateNormalSampler(IRandomNumberEngine engine, double[] mean, double[] choleskyFactor)
{
this.Length = mean.Length;
this.mean = mean;
this.choleskyFactor = choleskyFactor;
this.standardNormalSampler = new StandardNormalSampler(engine);
this.buffer = new double[this.mean.Length];
}
/// <summary>
/// Initializes a new instance of the <see cref="LogNormalSampler"/> class.
/// </summary>
/// <param name="generator">
/// The underlying bit generator to use.
/// </param>
/// <param name="mu">
/// The mean of the related normal distribution.
/// </param>
/// <param name="sigma">
/// The standard deviation of the related normal distribution.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <paramref name="generator"/> is null.
/// </exception>
/// <exception cref="ArgumentException">
/// If <paramref name="mu"/> is infinite or nan.
/// if <paramref name="sigma"/> is infinite or nan.
/// if <paramref name="sigma"/> is less than or equal to zero.
/// </exception>
public LogNormalSampler(IRandomNumberEngine generator, double mu, double sigma)
{
if (generator is null)
{
throw new ArgumentNullException(nameof(generator));
}
NormalDistribution.ValidateParameters(mu, sigma);
this.standardNormalSampler = new StandardNormalSampler(generator);
this.Mu = mu;
this.Sigma = sigma;
}
/// <summary>
/// Builds a new instance of a gaussian copula sampler.
/// </summary>
/// <param name="engine">
/// The random number engine to use as a ransom source.
/// </param>
/// <returns>
/// A new instance of a gaussian copula sampler.
/// </returns>
public GaussianCopulaSampler Build(IRandomNumberEngine engine)
{
return(new (engine, this.choleskyFactor, this.order));
}
/// <summary>
/// Initializes a new instance of the <see cref="InverseTransformSampler{T}"/> class.
/// </summary>
/// <param name="engine">
/// The random number engine.
/// </param>
/// <param name="distribution">
/// The reference distribution.
/// </param>
public InverseTransformSampler(IRandomNumberEngine engine, IDistribution <T> distribution)
{
this.engine = engine ?? throw new ArgumentNullException(nameof(engine));
this.Distribution = distribution ?? throw new ArgumentNullException(nameof(distribution));
}
/// <summary>
/// Initializes a new instance of the <see cref="StandardNormalSampler"/> class.
/// </summary>
/// <param name="engine">
/// The pseudo random engine used to generate random numbers.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <paramref name="engine"/> is null.
/// </exception>
public StandardNormalSampler(IRandomNumberEngine engine)
{
this.engine = engine ?? throw new ArgumentNullException(nameof(engine));
}
static IEnumerable <ulong> GetValues(IRandomNumberEngine engine) => Enumerable.Repeat(engine, 100).Select(e => e.NextULong());
private static void TestInvalidThrows <TException>(IRandomNumberEngine engine, double mu, double sigma)
where TException : Exception
{
Assert.Throws <TException>(
() => _ = new LogNormalSampler(engine, mu, sigma));
}
public static void Is(IRandomNumberEngine randomNumberEngine) => Randomizer.randomNumberEngine = randomNumberEngine;
/// <summary>
/// Builds a new instance of a multivariate normal sampler.
/// </summary>
/// <param name="engine">
/// The random number engine to use as a ransom source.
/// </param>
/// <returns>
/// A new instance of a multivariate normal sampler.
/// </returns>
public MultivariateNormalSampler Build(IRandomNumberEngine engine)
{
return(new (engine, this.mean, this.choleskyFactor));
}
