/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public double[] Generate(int samples)
{
var g = new AForge.Math.Random.GaussianGenerator(0, 1);
var u = Accord.Math.Tools.Random;
double[] r = new double[samples];
for (int i = 0; i < r.Length; i++)
{
double v = g.Next();
double y = v * v;
double x = mean + (mean * mean * y) / (2 * lambda) - (mean / (2 * lambda)) * Math.Sqrt(4 * mean * lambda * y + mean * mean * y * y);
double t = u.NextDouble();
if (t <= (mean) / (mean + x))
{
r[i] = x;
}
else
{
r[i] = (mean * mean) / x;
}
}
return(r);
}
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public override double Generate()
{
var g = new AForge.Math.Random.GaussianGenerator(
(float)mean, (float)stdDev, Accord.Math.Tools.Random.Next());
return(g.Next());
}
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
///
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public override double[] Generate(int samples)
{
double[] r = new double[samples];
var g = new AForge.Math.Random.GaussianGenerator(
(float)mean, (float)stdDev, Accord.Math.Tools.Random.Next());
for (int i = 0; i < r.Length; i++)
{
r[i] = g.Next();
}
return(r);
}
/// <summary>
/// Generates a random observation from the
/// Inverse Gaussian distribution with the given parameters.
/// </summary>
///
/// <param name="mean">The mean parameter mu.</param>
/// <param name="shape">The shape parameter lambda.</param>
///
/// <returns>A random double value sampled from the specified Uniform distribution.</returns>
///
public static double Random(double mean, double shape)
{
var u = Accord.Math.Tools.Random;
var g = new AForge.Math.Random.GaussianGenerator(0, 1, u.Next());
double v = g.Next();
double y = v * v;
double x = mean + (mean * mean * y) / (2 * shape) - (mean / (2 * shape)) * Math.Sqrt(4 * mean * shape * y + mean * mean * y * y);
double t = u.NextDouble();
if (t <= (mean) / (mean + x))
{
return(x);
}
return((mean * mean) / x);
}
/// <summary>
/// Random Gamma-distribution number generation
/// based on Marsaglia's Simple Method (2000).
/// </summary>
///
public static double Random(double d, double c)
{
var g = new AForge.Math.Random.GaussianGenerator(0,
1, Accord.Math.Tools.Random.Next());
// References:
//
// - Marsaglia, G. A Simple Method for Generating Gamma Variables, 2000
//
while (true)
{
// 2. Generate v = (1+cx)^3 with x normal
double x, t, v;
do
{
x = g.Next();
t = (1.0 + c * x);
v = t * t * t;
} while (v <= 0);
// 3. Generate uniform U
double U = Accord.Math.Tools.Random.NextDouble();
// 4. If U < 1-0.0331*x^4 return d*v.
double x2 = x * x;
if (U < 1 - 0.0331 * x2 * x2)
{
return(d * v);
}
// 5. If log(U) < 0.5*x^2 + d*(1-v+log(v)) return d*v.
if (Math.Log(U) < 0.5 * x2 + d * (1.0 - v + Math.Log(v)))
{
return(d * v);
}
// 6. Goto step 2
}
}
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public double[] Generate(int samples)
{
var g = new AForge.Math.Random.GaussianGenerator(0, 1);
var u = Accord.Math.Tools.Random;
double[] r = new double[samples];
for (int i = 0; i < r.Length; i++)
{
double v = g.Next();
double y = v * v;
double x = mean + (mean * mean * y) / (2 * lambda) - (mean / (2 * lambda)) * Math.Sqrt(4 * mean * lambda * y + mean * mean * y * y);
double t = u.NextDouble();
if (t <= (mean) / (mean + x))
r[i] = x;
else
r[i] = (mean * mean) / x;
}
return r;
}
/// <summary>
/// Random Gamma-distribution number generation
/// based on Marsaglia's Simple Method (2000).
/// </summary>
///
public static double Random(double d, double c)
{
var g = new AForge.Math.Random.GaussianGenerator(0,
1, Accord.Math.Tools.Random.Next());
// References:
//
// - Marsaglia, G. A Simple Method for Generating Gamma Variables, 2000
//
while (true)
{
// 2. Generate v = (1+cx)^3 with x normal
double x, t, v;
do
{
x = g.Next();
t = (1.0 + c * x);
v = t * t * t;
} while (v <= 0);
// 3. Generate uniform U
double U = Accord.Math.Tools.Random.NextDouble();
// 4. If U < 1-0.0331*x^4 return d*v.
double x2 = x * x;
if (U < 1 - 0.0331 * x2 * x2)
return d * v;
// 5. If log(U) < 0.5*x^2 + d*(1-v+log(v)) return d*v.
if (Math.Log(U) < 0.5 * x2 + d * (1.0 - v + Math.Log(v)))
return d * v;
// 6. Goto step 2
}
}
