Package 'PRNG'

Title: A Pseudo-Random Number Generator
Description: Provides functions for generating pseudo-random numbers that follow a uniform distribution [0,1]. Randomness tests were conducted using the National Institute of Standards and Technology test suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional tests. The sequence generated depends on the initial values and parameters. The package includes a linear congruence map as the decision map and three chaotic maps to generate the pseudo-random sequence, which follow a uniform distribution. Other distributions can be generated from the uniform distribution using the Inversion Principle Method and BOX-Muller transformation. Small perturbations in seed values result in entirely different sequences of numbers due to the sensitive nature of the maps being used. The chaotic nature of the maps helps achieve randomness in the generator. Additionally, the generator is capable of producing random bits.
Authors: Sajad Ahmad Mir [aut, cre] , Dr. Puneet Sharma [aut]
Maintainer: Sajad Ahmad Mir <[email protected]>
License: MIT + file LICENSE
Version: 0.0.2.1.3
Built: 2025-03-08 04:15:38 UTC
Source: https://github.com/meer-1/prng

Help Index

Baker map

Description

this is a chaotic map with the sensitive for the parameter value greater than 0.5

Usage

baker_map(x0, a)

Arguments

x0

seed value
a

parameter of the map range is greater than 0.5

Value

for 0<=x=<1/2 the map returns 2ax for 1/2<=x<=1 the map returns a(2x-1)mod1

Examples

baker_map(0.3,0.56)

Linear congruence map

Description

the map is a member of the family of the maps f(x)=(ax+b) mod(n)

Usage

linear_con(x0)

Arguments

x0

seed value

Value

the map gives an integer ax+b mod(n)

Examples

linear_con(5)

Logistic map

Description

This is the most used chaotic map . The map is sensitive for the value of the parameter greater than 3.568

Usage

logistic_map(x0, a)

Arguments

x0

the seed value range from 0 to 1
a

the parameter ranging from 3.5 to 4

Value

the map returns the a*x(1-x) for input x

Examples

logistic_map(0.26,3.5)

Random Bit generator

Description

this function generates random bits of desired length

Usage

prbits(n, Time = TRUE)

Arguments

n

number of bits required
Time

it is a boolean value of TRUE/FALSE if we want to generate time dependent random bits.i.e each time we call the function with same input different output will be generated.

Value

returns a vector of random bits of length n

Examples

prbits(2)
prbits(2)
prbits(2,Time=FALSE)
prbits(2,Time=FALSE)
 prbits(10)

Cauchy distribution

Description

This function generates random numbers from standard cauchy distribution

Usage

prcauchy(n, Time = TRUE)

Arguments

n

How many numbers we want
Time

time dependent or not

Value

a vector of n numbers from cauchy distribution

Examples

prcauchy(10)
prcauchy(10,Time=TRUE)
prcauchy(10,Time=TRUE)

Exponentail distribution

Description

This function generates random numbers from exponentail distribution

Usage

prexp(n, Time = TRUE)

Arguments

n

how many numbers we need
Time

time dependent or not

Value

a vector of n numbers from exponential distribution

Examples

prexp(10)
   prexp(10)
   prexp(10,FALSE)
   prexp(10,FALSE)

Generating numbers form Normal distribution here we use Box Muler transform to obtain normal random variable

Description

Generating numbers form Normal distribution here we use Box Muler transform to obtain normal random variable

Usage

prnorm(n)

Arguments

n

number required

Value

a list of pseudo random numbers from normal distribution

Examples

prnorm(10)
 prnorm(100)

Uniformly Pseudo random number generator

Description

this function generates random numbers which follow uniform distribution [0,1]

Usage

prunf(
  N = 100,
  Time = TRUE,
  n0 = 5,
  x00 = 0.5362,
  x01 = 0.357,
  x02 = 0.235,
  a1 = 3.69,
  a2 = 0.7
)

Arguments

N

How many numbers are required
Time

if enabled TRUE the numbers are time dependent
n0

seed value of linear congruence map it can take value of any natural number
x00

seed value of saw-tooth map values from 0 to 1
x01

seed value of logistic map values from 0 to 1
x02

seed value of baker map
a1

parameter of logistic map the value takes from 3.5 to 4
a2

parameter of baker map the value it takes values greater than or equalt to 0.5

Value

gives a vector of pseudo random numbers generated of desired length

Examples

prunf(10)
prunf(10,Time=TRUE)
prunf(10,Time=TRUE)
prunf(10,Time=TRUE)
prunf(10,2)
prunf(10,Time=TRUE,2)
prunf(10,Time=TRUE,2)

prunf(10,5,0.52)
 prunf(15,2,0.352)

  prunf(10,2,0.652,0.235)
   prunf(10,Time=TRUE,2,0.652,0.235)
  prunf(9,7,0.52,0.4235,0.389)
  prunf(10,Time=TRUE,2,0.752,0.235,0.351,3.8)

Saw tooth map

Description

saw tooth map is a family of maps as f(x)=b*x mod1

Usage

saw_tooth(x0)

Arguments

x0

seed value ranging from 0 to 1

Value

(3*x) mod(1)

Examples

saw_tooth(0.6)

stime function

Description

This function is used to generate a time of the system to be used for generating time dependent random numbers precise upto micro-seconds

Usage

stime()

Value

t fractional value of the time

Examples

stime()