NSoft.NFramework.Numerics.Distributions.Continuous.Normal.SampleBoxMuller C# (CSharp) Метод

        internal static Tuple<double, double> SampleBoxMuller(Random rnd) {
            var v1 = (2.0 * rnd.NextDouble()) - 1.0;
            var v2 = (2.0 * rnd.NextDouble()) - 1.0;
            var r = (v1 * v1) + (v2 * v2);
            while(r >= 1.0 || Math.Abs(r - 0.0) < double.Epsilon) {
                v1 = (2.0 * rnd.NextDouble()) - 1.0;
                v2 = (2.0 * rnd.NextDouble()) - 1.0;
                r = (v1 * v1) + (v2 * v2);
            }

            var fac = Math.Sqrt(-2.0 * Math.Log(r) / r);
            return new Tuple<double, double>(v1 * fac, v2 * fac);
        }
    }

 /// <summary>
 /// Samples standard student-t distributed random variables.
 /// </summary>
 /// <remarks>The algorithm is method 2 in section 5, chapter 9
 /// in L. Devroye's "Non-Uniform Random Variate Generation"</remarks>
 /// <param name="rnd">The random number generator to use.</param>
 /// <param name="dof">The degrees of freedom for the standard student-t distribution.</param>
 /// <returns>a random number from the standard student-t distribution.</returns>
 private static IEnumerable <double> DoSamples(Random rnd, double dof)
 {
     while (true)
     {
         var n = Normal.SampleBoxMuller(rnd).Item1;
         var g = Gamma.Sample(rnd, 0.5 * dof, 0.5);
         yield return(Math.Sqrt(dof / g) * n);
     }
 }