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.\" ========================================================================
.\"
.IX Title "RNG 3pm"
.TH RNG 3pm "2016-10-10" "perl v5.24.1" "User Contributed Perl Documentation"
.\" For nroff, turn off justification.  Always turn off hyphenation; it makes
.\" way too many mistakes in technical documents.
.if n .ad l
.nh
.SH "NAME"
PDL::GSL::RNG \- PDL interface to RNG and randist routines in GSL
.SH "DESCRIPTION"
.IX Header "DESCRIPTION"
This is an interface to the rng and randist packages present
in the \s-1GNU\s0 Scientific Library.
.SH "SYNOPSIS"
.IX Header "SYNOPSIS"
.Vb 2
\&   use PDL;
\&   use PDL::GSL::RNG;
\&
\&   $rng = PDL::GSL::RNG\->new(\*(Aqtaus\*(Aq);
\&
\&   $rng\->set_seed(time());
\&
\&   $a=zeroes(5,5,5)
\&
\&   $rng\->get_uniform($a); # inplace
\&
\&   $b=$rng\->get_uniform(3,4,5); # creates new pdl
.Ve
.SH "FUNCTIONS"
.IX Header "FUNCTIONS"
.SS "new"
.IX Subsection "new"
The new method initializes a new instance of the \s-1RNG.\s0
.PP
The available RNGs are:
.PP
.Vb 10
\& 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
.Ve
.PP
The last one (default) uses the environment variable \s-1GSL_RNG_TYPE.\s0
.PP
Note that only a few of these rngs are recommended for general
use. Please check the \s-1GSL\s0 documentation for more information.
.PP
Usage:
.PP
.Vb 1
\&   $blessed_ref = PDL::GSL::RNG\->new($RNG_name);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $rng = PDL::GSL::RNG\->new(\*(Aqtaus\*(Aq);
.Ve
.SS "set_seed"
.IX Subsection "set_seed"
Sets the \s-1RNG\s0 seed.
.PP
Usage:
.PP
.Vb 3
\&   $rng\->set_seed($integer);
\&   # or
\&   $rng = PDL::GSL::RNG\->new(\*(Aqtaus\*(Aq)\->set_seed($integer);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $rng\->set_seed(666);
.Ve
.SS "min"
.IX Subsection "min"
Return the minimum value generable by this \s-1RNG.\s0
.PP
Usage:
.PP
.Vb 1
\&   $integer = $rng\->min();
.Ve
.PP
Example:
.PP
.Vb 1
\&   $min = $rng\->min(); $max = $rng\->max();
.Ve
.SS "max"
.IX Subsection "max"
Return the maximum value generable by the \s-1RNG.\s0
.PP
Usage:
.PP
.Vb 1
\&   $integer = $rng\->max();
.Ve
.PP
Example:
.PP
.Vb 1
\&   $min = $rng\->min(); $max = $rng\->max();
.Ve
.SS "name"
.IX Subsection "name"
Returns the name of the \s-1RNG.\s0
.PP
Usage:
.PP
.Vb 1
\&   $string = $rng\->name();
.Ve
.PP
Example:
.PP
.Vb 1
\&   $name = $rng\->name();
.Ve
.SS "get"
.IX Subsection "get"
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. \fIget()\fR returns integer values
between a minimum and a maximum specific to every \s-1RNG.\s0
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->get($list_of_integers)
\&   $rng\->get($piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&   $a = zeroes 5,6;
\&   $o = $rng\->get(10,10); $rng\->get($a);
.Ve
.SS "get_int"
.IX Subsection "get_int"
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. \fIget_int()\fR returns integer values
between 0 and \f(CW$max\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->get($max, $list_of_integers)
\&   $rng\->get($max, $piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&   $a = zeroes 5,6; $max=100;
\&   $o = $rng\->get(10,10); $rng\->get($a);
.Ve
.SS "get_uniform"
.IX Subsection "get_uniform"
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. \fIget_uniform()\fR returns values 0<=x<1,
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->get_uniform($list_of_integers)
\&   $rng\->get_uniform($piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&   $a = zeroes 5,6; $max=100;
\&   $o = $rng\->get_uniform(10,10); $rng\->get_uniform($a);
.Ve
.SS "get_uniform_pos"
.IX Subsection "get_uniform_pos"
This function creates a piddle with given dimensions or accept an
existing piddle and fills it. \fIget_uniform_pos()\fR returns values 0<x<1,
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->get_uniform_pos($list_of_integers)
\&   $rng\->get_uniform_pos($piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&   $a = zeroes 5,6;
\&   $o = $rng\->get_uniform_pos(10,10); $rng\->get_uniform_pos($a);
.Ve
.SS "ran_shuffle"
.IX Subsection "ran_shuffle"
Shuffles values in piddle
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_shuffle($piddle);
.Ve
.SS "ran_shuffle_vec"
.IX Subsection "ran_shuffle_vec"
Shuffles values in piddle
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_shuffle_vec(@vec);
.Ve
.SS "ran_choose"
.IX Subsection "ran_choose"
Chooses values from \f(CW$inpiddle\fR to \f(CW$outpiddle\fR.
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_choose($inpiddle,$outpiddle);
.Ve
.SS "ran_choose_vec"
.IX Subsection "ran_choose_vec"
Chooses \f(CW$n\fR values from \f(CW@vec\fR.
.PP
Usage:
.PP
.Vb 1
\&   @chosen = $rng\->ran_choose_vec($n,@vec);
.Ve
.SS "ran_gaussian"
.IX Subsection "ran_gaussian"
Fills output piddle with random values from Gaussian distribution with mean zero and standard deviation \f(CW$sigma\fR.
.PP
Usage:
.PP
.Vb 2
\& $piddle = $rng\->ran_gaussian($sigma,[list of integers = output piddle dims]);
\& $rng\->ran_gaussian($sigma, $output_piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&  $o = $rng\->ran_gaussian($sigma,10,10);
\&  $rng\->ran_gaussian($sigma,$a);
.Ve
.SS "ran_gaussian_var"
.IX Subsection "ran_gaussian_var"
This method is similar to ran_gaussian except that it takes
the parameters of the distribution as a piddle and returns a piddle of equal
dimensions.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_gaussian_var($sigma_piddle);
\&   $rng\->ran_gaussian_var($sigma_piddle, $output_piddle);
.Ve
.PP
Example:
.PP
.Vb 2
\&   $sigma_pdl = rvals zeroes 11,11;
\&   $o = $rng\->ran_gaussian_var($sigma_pdl);
.Ve
.SS "ran_additive_gaussian"
.IX Subsection "ran_additive_gaussian"
Add Gaussian noise of given sigma to a piddle.
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_additive_gaussian($sigma,$piddle);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $rng\->ran_additive_gaussian(1,$image);
.Ve
.SS "ran_bivariate_gaussian"
.IX Subsection "ran_bivariate_gaussian"
Generates \f(CW$n\fR bivariate gaussian random deviates.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_bivariate_gaussian($sigma_x,$sigma_y,$rho,$n);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $o = $rng\->ran_bivariate_gaussian(1,2,0.5,1000);
.Ve
.SS "ran_poisson"
.IX Subsection "ran_poisson"
Fills output piddle by with random integer values from the Poisson distribution with mean \f(CW$mu\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_poisson($mu,[list of integers = output piddle dims]);
\&   $rng\->ran_poisson($mu,$output_piddle);
.Ve
.SS "ran_poisson_var"
.IX Subsection "ran_poisson_var"
Similar to ran_poisson except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_poisson_var($mu_piddle);
.Ve
.SS "ran_additive_poisson"
.IX Subsection "ran_additive_poisson"
Add Poisson noise of given \f(CW$mu\fR to a \f(CW$piddle\fR.
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_additive_poisson($mu,$piddle);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $rng\->ran_additive_poisson(1,$image);
.Ve
.SS "ran_feed_poisson"
.IX Subsection "ran_feed_poisson"
This method simulates shot noise, taking the values of piddle as
values for \f(CW$mu\fR to be fed in the poissonian \s-1RNG.\s0
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_feed_poisson($piddle);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $rng\->ran_feed_poisson($image);
.Ve
.SS "ran_bernoulli"
.IX Subsection "ran_bernoulli"
Fills output piddle with random values 0 or 1, the result of a Bernoulli trial with probability \f(CW$p\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_bernoulli($p,[list of integers = output piddle dims]);
\&   $rng\->ran_bernoulli($p,$output_piddle);
.Ve
.SS "ran_bernoulli_var"
.IX Subsection "ran_bernoulli_var"
Similar to ran_bernoulli except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_bernoulli_var($p_piddle);
.Ve
.SS "ran_beta"
.IX Subsection "ran_beta"
Fills output piddle with random variates from the beta distribution with parameters \f(CW$a\fR and \f(CW$b\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_beta($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_beta($a,$b,$output_piddle);
.Ve
.SS "ran_beta_var"
.IX Subsection "ran_beta_var"
Similar to ran_beta except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_beta_var($a_piddle, $b_piddle);
.Ve
.SS "ran_binomial"
.IX Subsection "ran_binomial"
Fills output piddle with random integer values from the binomial distribution, the number of
successes in \f(CW$n\fR independent trials with probability \f(CW$p\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_binomial($p,$n,[list of integers = output piddle dims]);
\&   $rng\->ran_binomial($p,$n,$output_piddle);
.Ve
.SS "ran_binomial_var"
.IX Subsection "ran_binomial_var"
Similar to ran_binomial except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_binomial_var($p_piddle, $n_piddle);
.Ve
.SS "ran_cauchy"
.IX Subsection "ran_cauchy"
Fills output piddle with random variates from the Cauchy distribution with scale parameter \f(CW$a\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_cauchy($a,[list of integers = output piddle dims]);
\&   $rng\->ran_cauchy($a,$output_piddle);
.Ve
.SS "ran_cauchy_var"
.IX Subsection "ran_cauchy_var"
Similar to ran_cauchy except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_cauchy_var($a_piddle);
.Ve
.SS "ran_chisq"
.IX Subsection "ran_chisq"
Fills output piddle with random variates from the chi-squared distribution with
\&\f(CW$nu\fR degrees of freedom.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_chisq($nu,[list of integers = output piddle dims]);
\&   $rng\->ran_chisq($nu,$output_piddle);
.Ve
.SS "ran_chisq_var"
.IX Subsection "ran_chisq_var"
Similar to ran_chisq except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_chisq_var($nu_piddle);
.Ve
.SS "ran_exponential"
.IX Subsection "ran_exponential"
Fills output piddle with random variates from the exponential distribution with mean \f(CW$mu\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_exponential($mu,[list of integers = output piddle dims]);
\&   $rng\->ran_exponential($mu,$output_piddle);
.Ve
.SS "ran_exponential_var"
.IX Subsection "ran_exponential_var"
Similar to ran_exponential except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_exponential_var($mu_piddle);
.Ve
.SS "ran_exppow"
.IX Subsection "ran_exppow"
Fills output piddle with random variates from the exponential power distribution with scale
parameter \f(CW$a\fR and exponent \f(CW$b\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_exppow($mu,$a,[list of integers = output piddle dims]);
\&   $rng\->ran_exppow($mu,$a,$output_piddle);
.Ve
.SS "ran_exppow_var"
.IX Subsection "ran_exppow_var"
Similar to ran_exppow except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_exppow_var($mu_piddle, $a_piddle);
.Ve
.SS "ran_fdist"
.IX Subsection "ran_fdist"
Fills output piddle with random variates from the F\-distribution with degrees
of freedom \f(CW$nu1\fR and \f(CW$nu2\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_fdist($nu1, $nu2,[list of integers = output piddle dims]);
\&   $rng\->ran_fdist($nu1, $nu2,$output_piddle);
.Ve
.SS "ran_fdist_var"
.IX Subsection "ran_fdist_var"
Similar to ran_fdist except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_fdist_var($nu1_piddle, $nu2_piddle);
.Ve
.SS "ran_flat"
.IX Subsection "ran_flat"
Fills output piddle with random variates from the flat (uniform) distribution from \f(CW$a\fR to \f(CW$b\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_flat($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_flat($a,$b,$output_piddle);
.Ve
.SS "ran_flat_var"
.IX Subsection "ran_flat_var"
Similar to ran_flat except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_flat_var($a_piddle, $b_piddle);
.Ve
.SS "ran_gamma"
.IX Subsection "ran_gamma"
Fills output piddle with random variates from the gamma distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_gamma($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_gamma($a,$b,$output_piddle);
.Ve
.SS "ran_gamma_var"
.IX Subsection "ran_gamma_var"
Similar to ran_gamma except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_gamma_var($a_piddle, $b_piddle);
.Ve
.SS "ran_geometric"
.IX Subsection "ran_geometric"
Fills output piddle with random integer values from the geometric distribution,
the number of independent trials with probability \f(CW$p\fR until the first success.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_geometric($p,[list of integers = output piddle dims]);
\&   $rng\->ran_geometric($p,$output_piddle);
.Ve
.SS "ran_geometric_var"
.IX Subsection "ran_geometric_var"
Similar to ran_geometric except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_geometric_var($p_piddle);
.Ve
.SS "ran_gumbel1"
.IX Subsection "ran_gumbel1"
Fills output piddle with random variates from the Type\-1 Gumbel distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_gumbel1($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_gumbel1($a,$b,$output_piddle);
.Ve
.SS "ran_gumbel1_var"
.IX Subsection "ran_gumbel1_var"
Similar to ran_gumbel1 except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_gumbel1_var($a_piddle, $b_piddle);
.Ve
.SS "ran_gumbel2"
.IX Subsection "ran_gumbel2"
Fills output piddle with random variates from the Type\-2 Gumbel distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_gumbel2($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_gumbel2($a,$b,$output_piddle);
.Ve
.SS "ran_gumbel2_var"
.IX Subsection "ran_gumbel2_var"
Similar to ran_gumbel2 except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_gumbel2_var($a_piddle, $b_piddle);
.Ve
.SS "ran_hypergeometric"
.IX Subsection "ran_hypergeometric"
Fills output piddle with random integer values from the hypergeometric distribution.
If a population contains \f(CW$n1\fR elements of type 1 and \f(CW$n2\fR elements of
type 2 then the hypergeometric distribution gives the probability of obtaining
\&\f(CW$x\fR elements of type 1 in \f(CW$t\fR samples from the population without replacement.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_hypergeometric($n1, $n2, $t,[list of integers = output piddle dims]);
\&   $rng\->ran_hypergeometric($n1, $n2, $t,$output_piddle);
.Ve
.SS "ran_hypergeometric_var"
.IX Subsection "ran_hypergeometric_var"
Similar to ran_hypergeometric except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_hypergeometric_var($n1_piddle, $n2_piddle, $t_piddle);
.Ve
.SS "ran_laplace"
.IX Subsection "ran_laplace"
Fills output piddle with random variates from the Laplace distribution with width \f(CW$a\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_laplace($a,[list of integers = output piddle dims]);
\&   $rng\->ran_laplace($a,$output_piddle);
.Ve
.SS "ran_laplace_var"
.IX Subsection "ran_laplace_var"
Similar to ran_laplace except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_laplace_var($a_piddle);
.Ve
.SS "ran_levy"
.IX Subsection "ran_levy"
Fills output piddle with random variates from the Levy symmetric stable
distribution with scale \f(CW$c\fR and exponent \f(CW$alpha\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_levy($mu,$a,[list of integers = output piddle dims]);
\&   $rng\->ran_levy($mu,$a,$output_piddle);
.Ve
.SS "ran_levy_var"
.IX Subsection "ran_levy_var"
Similar to ran_levy except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_levy_var($mu_piddle, $a_piddle);
.Ve
.SS "ran_logarithmic"
.IX Subsection "ran_logarithmic"
Fills output piddle with random integer values from the logarithmic distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_logarithmic($p,[list of integers = output piddle dims]);
\&   $rng\->ran_logarithmic($p,$output_piddle);
.Ve
.SS "ran_logarithmic_var"
.IX Subsection "ran_logarithmic_var"
Similar to ran_logarithmic except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_logarithmic_var($p_piddle);
.Ve
.SS "ran_logistic"
.IX Subsection "ran_logistic"
Fills output piddle with random random variates from the logistic distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_logistic($m,[list of integers = output piddle dims]u)
\&   $rng\->ran_logistic($m,$output_piddle)
.Ve
.SS "ran_logistic_var"
.IX Subsection "ran_logistic_var"
Similar to ran_logistic except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_logistic_var($m_piddle);
.Ve
.SS "ran_lognormal"
.IX Subsection "ran_lognormal"
Fills output piddle with random variates from the lognormal distribution with
parameters \f(CW$mu\fR (location) and \f(CW$sigma\fR (scale).
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_lognormal($mu,$sigma,[list of integers = output piddle dims]);
\&   $rng\->ran_lognormal($mu,$sigma,$output_piddle);
.Ve
.SS "ran_lognormal_var"
.IX Subsection "ran_lognormal_var"
Similar to ran_lognormal except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_lognormal_var($mu_piddle, $sigma_piddle);
.Ve
.SS "ran_negative_binomial"
.IX Subsection "ran_negative_binomial"
Fills output piddle with random integer values from the negative binomial
distribution, the number of failures occurring before \f(CW$n\fR successes in
independent trials with probability \f(CW$p\fR of success. Note that \f(CW$n\fR is
not required to be an integer.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_negative_binomial($p,$n,[list of integers = output piddle dims]);
\&   $rng\->ran_negative_binomial($p,$n,$output_piddle);
.Ve
.SS "ran_negative_binomial_var"
.IX Subsection "ran_negative_binomial_var"
Similar to ran_negative_binomial except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_negative_binomial_var($p_piddle, $n_piddle);
.Ve
.SS "ran_pareto"
.IX Subsection "ran_pareto"
Fills output piddle with random variates from the Pareto distribution of
order \f(CW$a\fR and scale \f(CW$b\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_pareto($a,$b,[list of integers = output piddle dims]);
\&   $rng\->ran_pareto($a,$b,$output_piddle);
.Ve
.SS "ran_pareto_var"
.IX Subsection "ran_pareto_var"
Similar to ran_pareto except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_pareto_var($a_piddle, $b_piddle);
.Ve
.SS "ran_pascal"
.IX Subsection "ran_pascal"
Fills output piddle with random integer values from the Pascal distribution.
The Pascal distribution is simply a negative binomial distribution
(see ran_negative_binomial) with an integer value of \f(CW$n\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_pascal($p,$n,[list of integers = output piddle dims]);
\&   $rng\->ran_pascal($p,$n,$output_piddle);
.Ve
.SS "ran_pascal_var"
.IX Subsection "ran_pascal_var"
Similar to ran_pascal except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_pascal_var($p_piddle, $n_piddle);
.Ve
.SS "ran_rayleigh"
.IX Subsection "ran_rayleigh"
Fills output piddle with random variates from the Rayleigh distribution with scale parameter \f(CW$sigma\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_rayleigh($sigma,[list of integers = output piddle dims]);
\&   $rng\->ran_rayleigh($sigma,$output_piddle);
.Ve
.SS "ran_rayleigh_var"
.IX Subsection "ran_rayleigh_var"
Similar to ran_rayleigh except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_rayleigh_var($sigma_piddle);
.Ve
.SS "ran_rayleigh_tail"
.IX Subsection "ran_rayleigh_tail"
Fills output piddle with random variates from the tail of the Rayleigh distribution
with scale parameter \f(CW$sigma\fR and a lower limit of \f(CW$a\fR.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_rayleigh_tail($a,$sigma,[list of integers = output piddle dims]);
\&   $rng\->ran_rayleigh_tail($a,$sigma,$output_piddle);
.Ve
.SS "ran_rayleigh_tail_var"
.IX Subsection "ran_rayleigh_tail_var"
Similar to ran_rayleigh_tail except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_rayleigh_tail_var($a_piddle, $sigma_piddle);
.Ve
.SS "ran_tdist"
.IX Subsection "ran_tdist"
Fills output piddle with random variates from the t\-distribution (\s-1AKA\s0 Student's
t\-distribution) with \f(CW$nu\fR degrees of freedom.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_tdist($nu,[list of integers = output piddle dims]);
\&   $rng\->ran_tdist($nu,$output_piddle);
.Ve
.SS "ran_tdist_var"
.IX Subsection "ran_tdist_var"
Similar to ran_tdist except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_tdist_var($nu_piddle);
.Ve
.SS "ran_ugaussian_tail"
.IX Subsection "ran_ugaussian_tail"
Fills output piddle with random variates from the upper tail of a Gaussian
distribution with \f(CW\*(C`standard deviation = 1\*(C'\fR (\s-1AKA\s0 unit Gaussian distribution).
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_ugaussian_tail($tail,[list of integers = output piddle dims]);
\&   $rng\->ran_ugaussian_tail($tail,$output_piddle);
.Ve
.SS "ran_ugaussian_tail_var"
.IX Subsection "ran_ugaussian_tail_var"
Similar to ran_ugaussian_tail except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_ugaussian_tail_var($tail_piddle);
.Ve
.SS "ran_weibull"
.IX Subsection "ran_weibull"
Fills output piddle with random variates from the Weibull distribution.
.PP
Usage:
.PP
.Vb 2
\&   $piddle = $rng\->ran_weibull($mu,$a,[list of integers = output piddle dims]);
\&   $rng\->ran_weibull($mu,$a,$output_piddle);
.Ve
.SS "ran_weibull_var"
.IX Subsection "ran_weibull_var"
Similar to ran_weibull except that it takes the distribution
parameters as a piddle and returns a piddle of equal dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_weibull_var($mu_piddle, $a_piddle);
.Ve
.SS "ran_dir"
.IX Subsection "ran_dir"
Returns \f(CW$n\fR random vectors in \f(CW$ndim\fR dimensions.
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_dir($ndim,$n);
.Ve
.PP
Example:
.PP
.Vb 1
\&   $o = $rng\->ran_dir($ndim,$n);
.Ve
.SS "ran_discrete_preproc"
.IX Subsection "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.
.PP
Usage:
.PP
.Vb 1
\&   $discrete_dist_handle = $rng\->ran_discrete_preproc($double_piddle_prob);
.Ve
.PP
Example:
.PP
.Vb 3
\&   $prob = pdl [0.1,0.3,0.6];
\&   $ddh = $rng\->ran_discrete_preproc($prob);
\&   $o = $rng\->ran_discrete($discrete_dist_handle,100);
.Ve
.SS "ran_discrete"
.IX Subsection "ran_discrete"
Is used to get the desired samples once a proper handle has been
enstablished (see \fIran_discrete_preproc()\fR).
.PP
Usage:
.PP
.Vb 1
\&   $piddle = $rng\->ran_discrete($discrete_dist_handle,$num);
.Ve
.PP
Example:
.PP
.Vb 3
\&   $prob = pdl [0.1,0.3,0.6];
\&   $ddh = $rng\->ran_discrete_preproc($prob);
\&   $o = $rng\->ran_discrete($discrete_dist_handle,100);
.Ve
.SS "ran_ver"
.IX Subsection "ran_ver"
Returns a piddle with \f(CW$n\fR values generated by the Verhulst map from \f(CW$x0\fR and
parameter \f(CW$r\fR.
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_ver($x0, $r, $n);
.Ve
.SS "ran_caos"
.IX Subsection "ran_caos"
Returns values from Verhuls map with \f(CW\*(C`$r=4.0\*(C'\fR and randomly chosen
\&\f(CW$x0\fR. The values are scaled by \f(CW$m\fR.
.PP
Usage:
.PP
.Vb 1
\&   $rng\->ran_caos($m,$n);
.Ve
.SH "BUGS"
.IX Header "BUGS"
Feedback is welcome. Log bugs in the \s-1PDL\s0 bug database (the
database is always linked from <http://pdl.perl.org/>).
.SH "SEE ALSO"
.IX Header "SEE ALSO"
\&\s-1PDL\s0
.PP
The \s-1GSL\s0 documentation is online at
<http://www.gnu.org/software/gsl/manual/html_node/>
.SH "AUTHOR"
.IX Header "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
\&\s-1COPYING\s0 in the \s-1PDL\s0 distribution. If this file is separated from the
\&\s-1PDL\s0 distribution, the copyright notice should be included in the file.
.PP
The \s-1GSL RNG\s0 and randist modules were written by James Theiler.