Random number generator of normal distribution in C #

Random

Box and Muller in 1958 years, the algorithm for generating the random variable of normal distribution by the uniformly distributed random variable is given. Set U1, U2 is a uniformly distributed random variable on the interval (0, 1), and is independent of each other.

The main reference is "numerical Recipes in C + + 2/e" p.292~p.294 and "simulation Modeling and Analysis 3/e" p.465~p.466.

Box and Muller in 1958 years, the algorithm for generating the random variable of normal distribution by the uniformly distributed random variable is given. Set U1, U2 is a uniformly distributed random variable on the interval (0, 1), and is independent of each other. Make

X1 = sqrt ( -2*log (U1)) * cos (2*PI*U2);
X2 = sqrt ( -2*log (U1)) * sin (2*PI*U2);

Then X1, X2 obey N (0,1) distribution, and independent of each other. It means that we get two independent N (0,1) random numbers with two independent U (0,1) random numbers.

Marsaglia and Bray introduced an improved algorithm in 1964 to avoid trigonometric functions. This improved algorithm is used for the following implementation code.

//
Gaussian Random Number Generator class
Ref. ' Numerical Recipes in C + + 2/e ', p.293 ~ p.294
//
public class Gaussianrng
{
int Iset;
Double Gset;
Random R1, R2;

Public Gaussianrng ()
{
R1 = new Random (unchecked (int) DateTime.Now.Ticks));
r2 = new Random (~unchecked (int) DateTime.Now.Ticks));
Iset = 0;
}

Public double Next ()
{
Double FAC, RSQ, V1, v2;
if (Iset = = 0) {
do {
V1 = 2.0 * R1. Nextdouble ()-1.0;
V2 = 2.0 * R2. Nextdouble ()-1.0;
RSQ = V1*v1 + v2*v2;
while (rsq >= 1.0 | | rsq = = 0.0);

FAC = MATH.SQRT ( -2.0*math.log (RSQ)/rsq);
Gset = V1*FAC;
Iset = 1;
return V2*FAC;
} else {
Iset = 0;
return gset;
}
}
}