mirror of
https://github.com/python/cpython
synced 2024-09-25 13:21:15 +00:00
Markup adjustments.
This commit is contained in:
Fred Drake
parent
cd14a87c02
commit
2eda4ca4df
|
|
@ -9,66 +9,68 @@ 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.
|
||||
equivalent to those in the \module{whrandom} module:
|
||||
\function{choice()}, \function{randint()}, \function{random()} and
|
||||
\function{uniform()}. See the documentation for the \module{whrandom}
|
||||
module for these functions.
|
||||
|
||||
The following functions specific to the \code{random} module are also
|
||||
The following functions specific to the \module{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.
|
||||
|
||||
\setindexsubitem{(in module random)}
|
||||
\begin{funcdesc}{betavariate}{alpha\, beta}
|
||||
Beta distribution. Conditions on the parameters are \code{alpha>-1}
|
||||
and \code{beta>-1}.
|
||||
\begin{funcdesc}{betavariate}{alpha, beta}
|
||||
Beta distribution. Conditions on the parameters are
|
||||
\code{\var{alpha}>-1} and \code{\var{beta}>-1}.
|
||||
Returned values will range between 0 and 1.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{cunifvariate}{mean\, arc}
|
||||
\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}.
|
||||
\code{\var{mean} - \var{arc}/2} and \code{\var{mean} + \var{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.
|
||||
Exponential distribution. \var{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.
|
||||
\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}.
|
||||
\begin{funcdesc}{gamma}{alpha, beta}
|
||||
Gamma distribution. (\emph{Not} the gamma function!) Conditions on
|
||||
the parameters are \code{\var{alpha}>-1} and \code{\var{beta}>0}.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{gauss}{mu\, sigma}
|
||||
\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.
|
||||
\function{normalvariate()} function defined below.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{lognormvariate}{mu\, sigma}
|
||||
\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}
|
||||
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}
|
||||
\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}
|
||||
\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}.
|
||||
$2\pi$.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{paretovariate}{alpha}
|
||||
|
|
@ -76,7 +78,7 @@ Pareto distribution. \var{alpha} is the shape parameter.
|
|||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{weibullvariate}{alpha, beta}
|
||||
Weibull distribution. \var{alpha} is the scale parameter, and
|
||||
Weibull distribution. \var{alpha} is the scale parameter and
|
||||
\var{beta} is the shape parameter.
|
||||
\end{funcdesc}
|
||||
|
||||
|
|
|
|||
|
|
@ -9,66 +9,68 @@ 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.
|
||||
equivalent to those in the \module{whrandom} module:
|
||||
\function{choice()}, \function{randint()}, \function{random()} and
|
||||
\function{uniform()}. See the documentation for the \module{whrandom}
|
||||
module for these functions.
|
||||
|
||||
The following functions specific to the \code{random} module are also
|
||||
The following functions specific to the \module{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.
|
||||
|
||||
\setindexsubitem{(in module random)}
|
||||
\begin{funcdesc}{betavariate}{alpha\, beta}
|
||||
Beta distribution. Conditions on the parameters are \code{alpha>-1}
|
||||
and \code{beta>-1}.
|
||||
\begin{funcdesc}{betavariate}{alpha, beta}
|
||||
Beta distribution. Conditions on the parameters are
|
||||
\code{\var{alpha}>-1} and \code{\var{beta}>-1}.
|
||||
Returned values will range between 0 and 1.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{cunifvariate}{mean\, arc}
|
||||
\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}.
|
||||
\code{\var{mean} - \var{arc}/2} and \code{\var{mean} + \var{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.
|
||||
Exponential distribution. \var{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.
|
||||
\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}.
|
||||
\begin{funcdesc}{gamma}{alpha, beta}
|
||||
Gamma distribution. (\emph{Not} the gamma function!) Conditions on
|
||||
the parameters are \code{\var{alpha}>-1} and \code{\var{beta}>0}.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{gauss}{mu\, sigma}
|
||||
\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.
|
||||
\function{normalvariate()} function defined below.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{lognormvariate}{mu\, sigma}
|
||||
\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}
|
||||
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}
|
||||
\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}
|
||||
\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}.
|
||||
$2\pi$.
|
||||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{paretovariate}{alpha}
|
||||
|
|
@ -76,7 +78,7 @@ Pareto distribution. \var{alpha} is the shape parameter.
|
|||
\end{funcdesc}
|
||||
|
||||
\begin{funcdesc}{weibullvariate}{alpha, beta}
|
||||
Weibull distribution. \var{alpha} is the scale parameter, and
|
||||
Weibull distribution. \var{alpha} is the scale parameter and
|
||||
\var{beta} is the shape parameter.
|
||||
\end{funcdesc}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue