nag_rngs_logarithmic (g05mdc) (PDF version)
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NAG C Library Manual

NAG Library Function Document

nag_rngs_logarithmic (g05mdc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_rngs_logarithmic (g05mdc) generates a vector of pseudorandom integers from the discrete logarithmic distribution with parameter a.

2  Specification

#include <nag.h>
#include <nagg05.h>
void  nag_rngs_logarithmic (Integer mode, double a, Integer n, Integer x[], Integer igen, Integer iseed[], double r[], NagError *fail)

3  Description

nag_rngs_logarithmic (g05mdc) generates n integers xi from a discrete logarithmic distribution, where the probability of xi=I is
Pxi=I=- aI I×log1-a ,  I=1,2,,
where 0<a<1.
The variates can be generated with or without using a search table and index. If a search table is used then it is stored with the index in a reference vector and subsequent calls to nag_rngs_logarithmic (g05mdc) with the same parameter value can then use this reference vector to generate further variates.
One of the initialization functions nag_rngs_init_repeatable (g05kbc) (for a repeatable sequence if computed sequentially) or nag_rngs_init_nonrepeatable (g05kcc) (for a non-repeatable sequence) must be called prior to the first call to nag_rngs_logarithmic (g05mdc).

4  References

Knuth D E (1981) The Art of Computer Programming (Volume 2) (2nd Edition) Addison–Wesley

5  Arguments

1:     modeIntegerInput
On entry: a code for selecting the operation to be performed by the function.
Set up reference vector only.
Generate variates using reference vector set up in a prior call to nag_rngs_logarithmic (g05mdc).
Set up reference vector and generate variates.
Generate variates without using the reference vector.
Constraint: mode=0, 1, 2 or 3.
2:     adoubleInput
On entry: a, the parameter of the logarithmic distribution.
Constraint: 0.0<a<1.0.
3:     nIntegerInput
On entry: n, the number of pseudorandom numbers to be generated.
Constraint: n1.
4:     x[n]IntegerOutput
On exit: the n pseudorandom numbers from the specified logarithmic distribution.
5:     igenIntegerInput
On entry: must contain the identification number for the generator to be used to return a pseudorandom number and should remain unchanged following initialization by a prior call to nag_rngs_init_repeatable (g05kbc) or nag_rngs_init_nonrepeatable (g05kcc).
6:     iseed[4]IntegerCommunication Array
On entry: contains values which define the current state of the selected generator.
On exit: contains updated values defining the new state of the selected generator.
7:     r[dim]doubleCommunication Array
Note: the dimension, dim, of the array r must be at least
  • 10+401-a  when mode3;
  • 1 otherwise.
On entry: if mode=1, the reference vector from the previous call to nag_rngs_logarithmic (g05mdc).
If mode=3, r is not referenced by nag_rngs_logarithmic (g05mdc).
On exit: the reference vector.
8:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

On entry, argument value had an illegal value.
a is such that the reference vector length would exceed integer range. We recommend setting mode=3. a=value.
On entry, mode=value.
Constraint: mode=0, 1, 2 or 3.
On entry, n=value.
Constraint: n1.
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
a is not the same as when r was set up in a previous call. Previous value of a=value and a=value.
On entry, a0.0 or a1.0: a=value.

7  Accuracy

Not applicable.

8  Further Comments


9  Example

This example prints five pseudorandom integers from a logarithmic distribution with parameter a=0.999, generated by a single call to nag_rngs_logarithmic (g05mdc), after initialization by nag_rngs_init_repeatable (g05kbc).

9.1  Program Text

Program Text (g05mdce.c)

9.2  Program Data


9.3  Program Results

Program Results (g05mdce.r)

nag_rngs_logarithmic (g05mdc) (PDF version)
g05 Chapter Contents
g05 Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012