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