G05TBF (PDF version)
G05 Chapter Contents
G05 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

G05TBF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

G05TBF generates a vector of pseudorandom logical values – .TRUE. with probability p and .FALSE. with probability 1-p.

2  Specification

SUBROUTINE G05TBF ( N, P, STATE, X, IFAIL)
INTEGER  N, STATE(*), IFAIL
REAL (KIND=nag_wp)  P
LOGICAL  X(N)

3  Description

G05TBF generates n logical values xi from the relation
yi<p  
where yi is a pseudorandom number from a uniform distribution over 0,1, generated by G05SAF using the values of STATE as input to this routine.
One of the initialization routines G05KFF (for a repeatable sequence if computed sequentially) or G05KGF (for a non-repeatable sequence) must be called prior to the first call to G05TBF.

4  References

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

5  Parameters

1:     N – INTEGERInput
On entry: n, the number of pseudorandom logical values to be generated.
Constraint: N0.
2:     P – REAL (KIND=nag_wp)Input
On entry: must contain the probability of G05TBF returning .TRUE..
Constraint: 0.0P1.0.
3:     STATE* – INTEGER arrayCommunication Array
Note: the actual argument supplied must be the array STATE supplied to the initialization routines G05KFF or G05KGF.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
4:     XN – LOGICAL arrayOutput
On exit: the n logical values.
5:     IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to 0, -1​ or ​1. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 is recommended. If the output of error messages is undesirable, then the value 1 is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is 0. When the value -1​ or ​1 is used it is essential to test the value of IFAIL on exit.
On exit: IFAIL=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6  Error Indicators and Warnings

If on entry IFAIL=0 or -1, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
IFAIL=1
On entry, N=value.
Constraint: N0.
IFAIL=2
On entry, P=value.
Constraint: 0.0P1.0.
IFAIL=3
On entry, STATE vector has been corrupted or not initialized.
IFAIL=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.
IFAIL=-399
Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.
IFAIL=-999
Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7  Accuracy

Not applicable.

8  Parallelism and Performance

G05TBF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

None.

10  Example

This example prints the first 20 pseudorandom logical values generated by G05TBF after initialization by G05KFF, when the probability of a .TRUE. value is 0.5.

10.1  Program Text

Program Text (g05tbfe.f90)

10.2  Program Data

Program Data (g05tbfe.d)

10.3  Program Results

Program Results (g05tbfe.r)


G05TBF (PDF version)
G05 Chapter Contents
G05 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015