RNG(3) User Contributed Perl Documentation RNG(3)NAMEPDL::GSL::RNG - PDL interface to RNG and randist routines in GSL
DESCRIPTION
This is an interface to the rng and randist packages present in the GNU
Scientific Library.
SYNOPSIS
use PDL;
use PDL::GSL::RNG;
$rng = PDL::GSL::RNG->new('taus');
$rng->set_seed(time());
$a=zeroes(5,5,5)
$rng->get_uniform($a); # inplace
$b=$rng->get_uniform(3,4,5); # creates new pdl
FUNCTIONSnew()
The new method initializes a new instance of the RNG.
The avaible RNGs are: coveyou cmrg fishman18 fishman20 fishman2x gfsr4
knuthran knuthran2 knuthran2002 lecuyer21 minstd mrg mt19937
mt19937_1999 mt19937_1998 r250 ran0 ran1 ran2 ran3 rand rand48
random128_bsd random128_glibc2 random128_libc5 random256_bsd
random256_glibc2 random256_libc5 random32_bsd random32_glibc2
random32_libc5 random64_bsd random64_glibc2 random64_libc5 random8_bsd
random8_glibc2 random8_libc5 random_bsd random_glibc2 random_libc5
randu ranf ranlux ranlux389 ranlxd1 ranlxd2 ranlxs0 ranlxs1 ranlxs2
ranmar slatec taus taus2 taus113 transputer tt800 uni uni32 vax
waterman14 zuf default The last one (default) uses the enviroment
variable GSL_RNG_TYPE. Note that only a few of these rngs are
recommended for general use. Please check the GSL documentation for
more information.
Usage:
$blessed_ref = PDL::GSL::RNG->new($RNG_name);
Example:
$rng = PDL::GSL::RNG->new('taus');
set_seed();
Sets the RNG seed.
Usage:
$rng->set_seed($integer);
Example:
$rng->set_seed(666);
min()
Return the minimum value generable by this RNG.
Usage:
$integer = $rng->min();
Example:
$min = $rng->min(); $max = $rng->max();
max()
Return the maximum value generable by the RNG.
Usage:
$integer = $rng->max();
Example:
$min = $rng->min(); $max = $rng->max();
name()
Returns the name of the RNG.
Usage:
$string = $rng->name();
Example:
$name = $rng->name();
get_uniform()
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. get_uniform() returns values 0<=x<1,
Usage:
$piddle = $rng->get_uniform($list_of_integers)
$rng->get_uniform($piddle);
Example:
$a = zeroes 5,6; $max=100;
$o = $rng->get_uniform(10,10); $rng->get_uniform($a);
get_uniform_pos()
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. get_uniform_pos() returns values 0<x<1,
Usage:
$piddle = $rng->get_uniform_pos($list_of_integers)
$rng->get_uniform_pos($piddle);
Example:
$a = zeroes 5,6;
$o = $rng->get_uniform_pos(10,10); $rng->get_uniform_pos($a);
get()
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. get() returns integer values beetween a
minimum and a maximum specific to evry RNG.
Usage:
$piddle = $rng->get($list_of_integers)
$rng->get($piddle);
Example:
$a = zeroes 5,6;
$o = $rng->get(10,10); $rng->get($a);
get_int()
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. get_int() returns integer values beetween
0 and $max.
Usage:
$piddle = $rng->get($max, $list_of_integers)
$rng->get($max, $piddle);
Example:
$a = zeroes 5,6; $max=100;
$o = $rng->get(10,10); $rng->get($a);
ran_gaussian()
These functions return random deviates from given distribution.
The general form is
ran_[distrib](args)
where distrib can be any of the ones shown below.
They accept the parameters of the distribution and a specification of
where to put output. This spec can be in form of list of integers that
specify the dimensions of the ouput piddle or an existing piddle that
will be filled with values inplace.
Usage:
# gaussian dist
$piddle = $rng->ran_gaussian($sigma,[list of integers]);
$rng->ran_gaussian($sigma,$piddle);
# gaussian tail
$piddle = $rng->ran_ugaussian_tail($tail,[list of integers]);
$rng->ran_ugaussian_tail($tail,$piddle);
# exponential dist
$piddle = $rng->ran_exponential($mu,[list of integers]);
$rng->ran_exponential($mu,$piddle);
# laplacian dist
$piddle = $rng->ran_laplace($mu,[list of integers]);
$rng->ran_laplace($mu,$piddle);
$piddle = $rng->ran_exppow($mu,$a,[list of integers]);
$rng->ran_exppow($mu,$a,$piddle);
$piddle = $rng->ran_cauchy($mu,[list of integers]);
$rng->ran_cauchy($mu,$piddle);
$piddle = $rng->ran_rayleigh($sigma,[list of integers]);
$rng->ran_rayleigh($sigma,$piddle);
$piddle = $rng->ran_rayleigh_tail($a,$sigma,[list of integers]);
$rng->ran_rayleigh_tail($a,$sigma,$piddle);
$piddle = $rng->ran_levy($mu,$a,[list of integers]);
$rng->ran_levy($mu,$a,$piddle);
$piddle = $rng->ran_gamma($a,$b,[list of integers]);
$rng->ran_gamma($a,$b,$piddle);
$piddle = $rng->ran_flat($a,$b,[list of integers]);
$rng->ran_flat($a,$b,$piddle);
$piddle = $rng->ran_lognormal($zeta, $sigma,[list of integers]);
$rng->ran_lognormal($zeta, $sigma,$piddle);
$piddle = $rng->ran_chisq($nu,[list of integers]);
$rng->ran_chisq($nu,$piddle);
$piddle = $rng->ran_fdist($nu1, $nu2,[list of integers]);
$rng->ran_fdist($nu1, $nu2,$piddle);
$piddle = $rng->ran_tdist($nu,[list of integers]);
$rng->ran_tdist($nu,$piddle);
$piddle = $rng->ran_beta($a,$b,[list of integers]);
$rng->ran_beta($a,$b,$piddle);
$piddle = $rng->ran_logistic($m,[list of integers]u)
$rng->ran_logistic($m,$piddleu)
$piddle = $rng->ran_pareto($a,$b,[list of integers]);
$rng->ran_pareto($a,$b,$piddle);
$piddle = $rng->ran_weibull($mu,$a,[list of integers]);
$rng->ran_weibull($mu,$a,$piddle);
$piddle = $rng->ran_gumbel1($a,$b,[list of integers]);
$rng->ran_gumbel1($a,$b,$piddle);
$piddle = $rng->ran_gumbel2($a,$b,[list of integers]);
$rng->ran_gumbel2($a,$b,$piddle);
$piddle = $rng->ran_poisson($mu,[list of integers]);
$rng->ran_poisson($mu,$piddle);
$piddle = $rng->ran_bernoulli($p,[list of integers]);
$rng->ran_bernoulli($p,$piddle);
$piddle = $rng->ran_binomial($p,$n,[list of integers]);
$rng->ran_binomial($p,$n,$piddle);
$piddle = $rng->ran_negative_binomial($p,$n,[list of integers]);
$rng->ran_negative_binomial($p,$n,$piddle);
$piddle = $rng->ran_pascal($p,$n,[list of integers]);
$rng->ran_pascal($p,$n,$piddle);
$piddle = $rng->ran_geometric($p,[list of integers]);
$rng->ran_geometric($p,$piddle);
$piddle = $rng->ran_hypergeometric($n1, $n2, $t,[list of integers]);
$rng->ran_hypergeometric($n1, $n2, $t,$piddle);
$piddle = $rng->ran_logarithmic($p,[list of integers]);
$rng->ran_logarithmic($p,$piddle);
Example:
$o = $rng->ran_gaussian($sigma,10,10);
$rng->ran_gaussian($sigma,$a);
ran_gaussian_var()
This method is similar to ran_[distrib]() except that it takes the
parameters of the distribution as a piddle and returns a piddle of
equal dimensions. Of course, you can use the same set of distributions
as in the previous method (see also the ran_gaussian entry above).
Usage:
$piddle = $rng->ran_[distribution]_var($distr_parameters_list,$piddle_dim_list);
$rng->ran_[distribution]_var($distr_parameters_list,$piddle);
Example:
$sigma_pdl = rvals zeroes 11,11;
$o = $rng->ran_gaussian_var($sigma_pdl);
ran_additive_gaussian()
Add Gaussian noise of given sigma to a piddle.
Usage:
$rng->ran_additive_gaussian($sigma,$piddle);
Example:
$rng->ran_additive_gaussian(1,$image);
ran_additive_poisson()
Add Poisson noise of given sigma to a piddle.
Usage:
$rng->ran_additive_poisson($mu,$piddle);
Example:
$rng->ran_additive_poisson(1,$image);
ran_feed_poisson()
This method simulates shot noise, taking the values of piddle as values
for mu to be fed in the poissonian RNG.
Usage:
$rng->ran_feed_poisson($piddle);
Example:
$rng->ran_feed_poisson($image);
ran_bivariate_gaussian()
Generates $n bivariate gaussian random deviates.
Usage:
$piddle = $rng->ran_bivariate_gaussian($sigma_x,$sigma_y,$rho,$n);
Example:
$o = $rng->ran_bivariate_gaussian(1,2,0.5,1000);
ran_dir()
Returns $n random vectors in $ndim dimensions.
Usage:
$piddle = $rng->ran_dir($ndim,$n);
Example:
$o = $rng->ran_dir($ndim,$n);
ran_discrete_preproc()
This method returns a handle that must be used when calling
ran_discrete(). You specify the probability of the integer number that
are returned by ran_discrete().
Usage:
$discrete_dist_handle = $rng->ran_discrete_preproc($double_piddle_prob);
Example:
$prob = pdl [0.1,0.3,0.6];
$ddh = $rng->ran_discrete_preproc($prob);
$o = $rng->ran_discrete($discrete_dist_handle,100);
ran_discrete()
Is used to get the desired samples once a proper handle has been
enstablished (see ran_discrete_preproc()).
Usage:
$piddle = $rng->ran_discrete($discrete_dist_handle,$num);
Example:
$prob = pdl [0.1,0.3,0.6];
$ddh = $rng->ran_discrete_preproc($prob);
$o = $rng->ran_discrete($discrete_dist_handle,100);
ran_shuffle()
Shuffles values in piddle
Usage:
$rng->ran_shuffle($piddle);
ran_shuffle_vec()
Shuffles values in piddle
Usage:
$rng->ran_shuffle_vec(@vec);
ran_choose()
Chooses values from $inpiddle to $outpiddle.
Usage:
$rng->ran_choose($inpiddle,$outpiddle);
ran_choose_vec()
Chooses $n values from @vec.
Usage:
@choosen = $rng->ran_choose_vec($n,@vec);
ran_ver()
Returns a piddle with $n values generated by the Verhulst map from $x0
and paramater $r.
Usage:
$rng->ran_ver($x0, $r, $n);
ran_caos()
Returns values from Verhuls map with $r=4.0 and randomly choosen $x0.
The values are scaled by $m.
Usage:
$rng->ran_caos($m,$n);
BUGS
Feedback is welcome. Log bugs in the PDL bug database (the database is
always linked from <http://pdl.perl.org/>).
SEE ALSO
PDL
The GSL documentation is online at
<http://www.gnu.org/software/gsl/manual/html_node/>
AUTHOR
This file copyright (C) 1999 Christian Pellegrin
<chri@infis.univ.trieste.it> Docs mangled by C. Soeller. All rights
reserved. There is no warranty. You are allowed to redistribute this
software / documentation under certain conditions. For details, see the
file COPYING in the PDL distribution. If this file is separated from
the PDL distribution, the copyright notice should be included in the
file.
The GSL RNG and randist modules were written by James Theiler.
perl v5.18.1 2014-01-17 RNG(3)
