Sampling Functions #

Random Number Generation #

File position: /EMOC/vendor/random.h and /EMOC/vendor/random.cpp

Random number generation is a basic function for evolutionary algorithm, EMOC provides several random number related functions as below:

double randomperc()

Fetch a single random number between 0.0 and 1.0.

Parameter:
	void
Returns:
	rand: double A random number between 0.0 and 1.0.

int rnd(int low, int high)

Return a random integer between low and high.

Parameter:
	low: int, default=None Lower boundary of random integer. high: int, default=None Upper boundary of random integer.
Returns:
	rand: double A random integer between low and high.

double rndreal(double low, double high)

Return a random real number between low and high.

Parameter:
	low: double, default=None Lower boundary of random double. high: double, default=None Upper boundary of random double.
Returns:
	rand: double A random double between low and high.

File position: /EMOC/src/core/utility.h and /EMOC/src/core/utility.cpp

There are also some sampling function for different distribution:

double CauchyRandom(double location, double scale)

Return a sample value subject to Cauchy distribution.

Parameter:
	location: double, default=None The location parameter of Cauchy distribution. scale: double, default=None The scale parameter of Cauchy distribution.
Returns:
	sample: double A random sample from Cauchy distribution.

double BetaRandom(double a, double b)

Return a sample value subject to Beta distribution.

Parameter:
	a: double, default=None The alpha parameter of Beta distribution. b: double, default=None The beta parameter of Beta distribution.
Returns:
	sample: double A random sample from Beta distribution.

double GaussianRandom(double mean, double stdev)

Return a sample value subject to Gaussian distribution.

Parameter:
	mean: double, default=None The mean parameter of Gaussian distribution. stdev: double, default=None The standard deviation parameter of Gaussian distribution.
Returns:
	sample: double A random sample from Gaussian distribution.