问题
I have a code here which generates random numbers having a mean 0f 1 and std deviation of 0.5. but how do i modify this code so that i can denerate gaussian random numbers of any given mean and variance?
#include <stdlib.h>
#include <math.h>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
double drand() /* uniform distribution, (0..1] */
{
return (rand()+1.0)/(RAND_MAX+1.0);
}
double random_normal()
/* normal distribution, centered on 0, std dev 1 */
{
return sqrt(-2*log(drand())) * cos(2*M_PI*drand());
}
int main()
{
int i;
double rands[1000];
for (i=0; i<1000; i++)
rands[i] = 1.0 + 0.5*random_normal();
return 0;
}
回答1:
I have a code here which generates random numbers having a mean 0f 1 and std deviation of 0.5. but how do i modify this code so that i can denerate gaussian random numbers of any given mean and variance?
If x
is a random variable from a Gaussian distribution with mean μ
and standard deviation σ
, then αx+β
will have mean αμ+β
and standard deviation |α|σ
.
In fact, the code you posted already does this transformation. It starts with a random variable with mean 0 and standard deviation 1 (obtained from the function random_normal
, which implements the Box–Muller transform), and then transforms it to a random variable with mean 1 and standard deviation 0.5 (in the rands
array) via multiplication and addition:
double random_normal(); /* normal distribution, centered on 0, std dev 1 */
rands[i] = 1.0 + 0.5*random_normal();
回答2:
There are several ways to do this- all of which basically involve transforming/mapping your uniformly distributed values to a normal/gaussian distribution. A Ziggurat transformation is probably your best bet.
One thing to keep in mind- the quality of your end distribution is only as good as your RNG, so be sure to use a quality random number generator (e.g.- Mersenne twister) if the quality of the generated values is important.
来源:https://stackoverflow.com/questions/7034930/how-to-generate-gaussian-pseudo-random-numbers-in-c-for-a-given-mean-and-varianc