/// <summary>
/// Initializes a new normal distribution with the given mean and standard deviation.
/// </summary>
/// <param name="mu">The mean.</param>
/// <param name="sigma">The standard deviation, which must be positive.</param>
/// <exception cref="ArgumentOutOfRangeException"><paramref name="sigma"/> is less than or equal to zero.</exception>
public NormalDistribution(double mu, double sigma)
{
if (sigma <= 0.0) throw new ArgumentOutOfRangeException("sigma");
this.mu = mu;
this.sigma = sigma;
this.normalRng = DeviateGeneratorFactory.GetNormalGenerator();
}
public BetaFromGammaGenerator(IDeviateGenerator alphaGenerator, IDeviateGenerator betaGenerator)
{
Debug.Assert(alphaGenerator != null);
Debug.Assert(betaGenerator != null);
this.alphaGenerator = alphaGenerator;
this.betaGenerator = betaGenerator;
}
/// <summary>
/// Initializes a new Cauchy distribution.
/// </summary>
/// <param name="mu">The centroid of the distribution.</param>
/// <param name="gamma">The width parameter of the distribution.</param>
public CauchyDistribution(double mu, double gamma)
{
if (gamma <= 0.0) throw new ArgumentNullException("gamma");
this.mu = mu;
this.gamma = gamma;
this.cauchyRng = DeviateGeneratorFactory.GetCauchyGenerator();
}
public BetaFromGammaGenerator(IDeviateGenerator alphaGenerator, IDeviateGenerator betaGenerator)
{
if (alphaGenerator == null) throw new ArgumentNullException("alphaGenerator");
if (betaGenerator == null) throw new ArgumentNullException("betaGenerator");
this.alphaGenerator = alphaGenerator;
this.betaGenerator = betaGenerator;
}
public MarsagliaTsangGammaGenerator(IDeviateGenerator normalGenerator, double alpha)
{
Debug.Assert(alpha >= 1.0);
this.normalGenerator = normalGenerator;
a = alpha;
a1 = a - 1.0 / 3.0;
a2 = 1.0 / Math.Sqrt(9.0 * a1);
}
/// <summary>
/// Initializes a new normal distribution with the given mean and standard deviation.
/// </summary>
/// <param name="mu">The mean.</param>
/// <param name="sigma">The standard deviation, which must be positive.</param>
/// <exception cref="ArgumentOutOfRangeException"><paramref name="sigma"/> is less than or equal to zero.</exception>
public NormalDistribution(double mu, double sigma)
{
if (sigma <= 0.0)
{
throw new ArgumentOutOfRangeException(nameof(sigma));
}
this.mu = mu;
this.sigma = sigma;
this.normalRng = DeviateGeneratorFactory.GetNormalGenerator();
}
public MarsagliaTsangGammaGenerator(IDeviateGenerator normalGenerator, double alpha)
{
if (alpha < 1.0)
{
throw new ArgumentOutOfRangeException("alpha");
}
this.normalGenerator = normalGenerator;
a = alpha;
a1 = a - 1.0 / 3.0;
a2 = 1.0 / Math.Sqrt(9.0 * a1);
}
public BetaFromGammaGenerator(IDeviateGenerator alphaGenerator, IDeviateGenerator betaGenerator)
{
if (alphaGenerator == null)
{
throw new ArgumentNullException("alphaGenerator");
}
if (betaGenerator == null)
{
throw new ArgumentNullException("betaGenerator");
}
this.alphaGenerator = alphaGenerator;
this.betaGenerator = betaGenerator;
}
/// <summary>
/// Initializes a new β distribution.
/// </summary>
/// <param name="alpha">The left shape parameter, which controls the form of the distribution near x=0.</param>
/// <param name="beta">The right shape parameter, which controls the form of the distribution near x=1.</param>
/// <remarks>
/// <para>The <paramref name="alpha"/> shape parameter controls the form of the distribution near x=0. The
/// <paramref name="beta"/> shape parameter controls the form of the distribution near z=1. If a shape parameter
/// is less than one, the PDF diverges on the side of the distribution it controls. If a shape parameter
/// is greater than one, the PDF goes to zero on the side of the distribution it controls. If the left and right
/// shapre parameters are equal, the distribution is symmetric about x=1/2.</para>
/// </remarks>
/// <seealso href="http://en.wikipedia.org/wiki/Beta_distribution" />
public BetaDistribution(double alpha, double beta)
{
if (alpha <= 0.0) throw new ArgumentOutOfRangeException("alpha");
if (beta <= 0.0) throw new ArgumentOutOfRangeException("beta");
this.alpha = alpha;
this.beta = beta;
// cache value of B(alpha, beta) to avoid having to re-calculate it whenever needed
this.bigB = AdvancedMath.Beta(alpha, beta);
// get a beta generator
this.betaRng = DeviateGeneratorFactory.GetBetaGenerator(alpha, beta);
// get a beta inverter
this.betaInverter = new BetaInverter(alpha, beta);
}
/// <summary>
/// Initializes a new instance of a Gamma distribution with the given parameters.
/// </summary>
/// <param name="shape">The shape parameter, which must be positive.</param>
/// <param name="scale">The scale parameter, which must be positive.</param>
public GammaDistribution(double shape, double scale)
{
if (shape <= 0.0) throw new ArgumentOutOfRangeException("shape");
if (scale <= 0.0) throw new ArgumentOutOfRangeException("scale");
a = shape;
s = scale;
// depending on size of a, store Gamma(a) or -Ln(Gamma(a)) for future use
if (a < 64.0) {
ga = AdvancedMath.Gamma(a);
} else {
ga = -AdvancedMath.LogGamma(a);
}
gammaRng = DeviateGeneratorFactory.GetGammaGenerator(a);
}
public MarsagliaTsangGammaGenerator(IDeviateGenerator normalGenerator, double alpha)
{
if (alpha < 1.0) throw new ArgumentOutOfRangeException("alpha");
this.normalGenerator = normalGenerator;
a = alpha;
a1 = a - 1.0 / 3.0;
a2 = 1.0 / Math.Sqrt(9.0 * a1);
}
