# cpython-withatomic / Doc / librandom.tex

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 \section{Standard Module \sectcode{random}} \label{module-random} \stmodindex{random} 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 \code{whrandom} module: \code{choice}, \code{randint}, \code{random}, \code{uniform}. See the documentation for the \code{whrandom} module for these functions. The following functions specific to the \code{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. \renewcommand{\indexsubitem}{(in module random)} \begin{funcdesc}{betavariate}{alpha\, beta} Beta distribution. Conditions on the parameters are \code{alpha>-1} and \code{beta>-1}. Returned values will range between 0 and 1. \end{funcdesc} \begin{funcdesc}{cunifvariate}{mean\, arc} Circular uniform distribution. \var{mean} is the mean angle, and \var{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 \code{pi}. Returned values will range between \code{mean - arc/2} and \code{mean + arc/2}. \end{funcdesc} \begin{funcdesc}{expovariate}{lambd} Exponential distribution. \var{lambd} is 1.0 divided by the desired mean. (The parameter would be called lambda'', but that's also a reserved word in Python.) Returned values will range from 0 to positive infinity. \end{funcdesc} \begin{funcdesc}{gamma}{alpha\, beta} Gamma distribution. (\emph{Not} the gamma function!) Conditions on the parameters are \code{alpha>-1} and \code{beta>0}. \end{funcdesc} \begin{funcdesc}{gauss}{mu\, sigma} Gaussian distribution. \var{mu} is the mean, and \var{sigma} is the standard deviation. This is slightly faster than the \code{normalvariate} function defined below. \end{funcdesc} \begin{funcdesc}{lognormvariate}{mu\, sigma} Log normal distribution. If you take the natural logarithm of this distribution, you'll get a normal distribution with mean \var{mu} and standard deviation \var{sigma} \var{mu} can have any value, and \var{sigma} must be greater than zero. \end{funcdesc} \begin{funcdesc}{normalvariate}{mu\, sigma} Normal distribution. \var{mu} is the mean, and \var{sigma} is the standard deviation. \end{funcdesc} \begin{funcdesc}{vonmisesvariate}{mu\, kappa} \var{mu} is the mean angle, expressed in radians between 0 and pi, and \var{kappa} is the concentration parameter, which must be greater then or equal to zero. If \var{kappa} is equal to zero, this distribution reduces to a uniform random angle over the range 0 to \code{2*pi}. \end{funcdesc} \begin{seealso} \seemodule{whrandom}{the standard Python random number generator} \end{seealso}