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Batuhan Osman TASKAYA
cpython
Commits
2eda4ca4
Kaydet (Commit)
2eda4ca4
authored
Mar 08, 1998
tarafından
Fred Drake
Dosyalara gözat
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librandom.tex
Doc/lib/librandom.tex
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librandom.tex
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Doc/lib/librandom.tex
Dosyayı görüntüle @
2eda4ca4
...
...
@@ -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
\
cod
e
{
random
}
module are also
The following functions specific to the
\
modul
e
{
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}
...
...
Doc/librandom.tex
Dosyayı görüntüle @
2eda4ca4
...
...
@@ -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
\
cod
e
{
random
}
module are also
The following functions specific to the
\
modul
e
{
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}
...
...
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