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 random module supports the Random Number
Generator interface, described in section . This
interface of the module, as well as the distribution-specific
functions described below, all use the pseudo-random generator
provided by the whrandom module.
The following functions are defined to support specific distributions,
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. These are expected to become part of the Random
Number Generator interface in a future release.
- betavariate (alpha, beta)
-
Beta distribution. Conditions on the parameters are
alpha > -1 and beta > -1.
Returned values 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.
Subsections
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