This module implements pseudo-random number generators for various
distributions: on the real line, there are functions to compute normal
or Gaussian, lognormal, negative exponential, gamma, and beta
distributions. For generating distribution of angles, the circular
uniform and von Mises distributions are available.
The module exports the following functions, which are exactly
equivalent to those in the whrandom module:
choice(), randint(), random() and
uniform(). See the documentation for the whrandom
module for these functions.
The following functions specific to the random module are also
defined, and all return real values. Function parameters are named
after the corresponding variables in the distribution's equation, as
used in common mathematical practice; most of these equations can be
found in any statistics text.
- betavariate (alpha, beta)
-
Beta distribution. Conditions on the parameters are
alpha >- 1 and beta > -1.
Returned values will range between 0 and 1.
- cunifvariate (mean, arc)
-
Circular uniform distribution. mean is the mean angle, and
arc is the range of the distribution, centered around the mean
angle. Both values must be expressed in radians, and can range
between 0 and pi. Returned values will range between
mean - arc/2 and mean + arc/2.
- expovariate (lambd)
-
Exponential distribution. lambd is 1.0 divided by the desired
mean. (The parameter would be called ``lambda'', but that is a
reserved word in Python.) Returned values will range from 0 to
positive infinity.
- gamma (alpha, beta)
-
Gamma distribution. (Not the gamma function!) Conditions on
the parameters are alpha > -1 and beta > 0.
- gauss (mu, sigma)
-
Gaussian distribution. mu is the mean, and sigma is the
standard deviation. This is slightly faster than the
normalvariate() function defined below.
- lognormvariate (mu, sigma)
-
Log normal distribution. If you take the natural logarithm of this
distribution, you'll get a normal distribution with mean mu and
standard deviation sigma. mu can have any value, and sigma
must be greater than zero.
- normalvariate (mu, sigma)
-
Normal distribution. mu is the mean, and sigma is the
standard deviation.
- vonmisesvariate (mu, kappa)
-
mu is the mean angle, expressed in radians between 0 and 2*pi,
and kappa is the concentration parameter, which must be greater
than or equal to zero. If kappa is equal to zero, this
distribution reduces to a uniform random angle over the range 0 to
2*pi.
- paretovariate (alpha)
-
Pareto distribution. alpha is the shape parameter.
- weibullvariate (alpha, beta)
-
Weibull distribution. alpha is the scale parameter and
beta is the shape parameter.
See Also:
Module whrandom (the standard Python random number generator)
