math::Random Class Reference

Public Member Functions
function	_randbelow (local n)
function	_set_random (local generator, local seeder)
function	betavariate (local alpha, local beta)
function	choice (local seq)
function	expovariate (local lambd)
function	gammavariate (local alpha, local beta)
function	gauss (local mu, local sigma)
function	lognormvariate (local mu, local sigma)
function	normalvariate (local mu, local sigma)
function	paretovariate (local alpha)
function	randint (local a, local b)
function	random ()
function	randrange (local start, local stop, local step)
function	sample (local population, local k)
function	seed (local a)
function	shuffle (local x, local random)
function	triangular (local low, local high, local mode)
function	uniform (local a, local b)
function	vonmisesvariate (local mu, local kappa)
function	weibullvariate (local alpha, local beta)

Member Function Documentation

_randbelow()

function _randbelow

(

local

n

)

Return a random int in the range [0, n)

Parameters

n

number

Returns

number

_set_random()

function _set_random	(	local	generator,
		local	seeder
	)

betavariate()

function betavariate	(	local	alpha,
		local	beta
	)

Beta distribution.

Conditions on the parameters are alpha > 0 and beta > 0.

Returned values range between 0 and 1.

Parameters

alpha	number
beta	number

Returns

number

choice()

function choice

(

local

seq

)

Choose a random element from a non-empty sequence.

expovariate()

function expovariate

(

local

lambd

)

Exponential distribution.

lambd is 1.0 divided by the desired mean. It should be

nonzero. (The parameter would be called "lambda", but that is

a reserved word in Python.) Returned values range from 0 to

positive infinity if lambd is positive, and from negative

infinity to 0 if lambd is negative.

Parameters

lambd

number

Returns

number

gammavariate()

function gammavariate	(	local	alpha,
		local	beta
	)

Gamma distribution. Not the gamma function!

Conditions on the parameters are alpha > 0 and beta > 0.

The probability distribution function is:

         x ** (alpha - 1) * math.exp(-x / beta)

pdf(x) = -----------------------------------—

        math.gamma(alpha) * beta ** alpha

Parameters

alpha	number
beta	number

Returns

number

gauss()

function gauss	(	local	mu,
		local	sigma
	)

Gaussian distribution.

mu is the mean, and sigma is the standard deviation. This is

slightly faster than the normalvariate() function.

Parameters

mu	number
sigma	number

Returns

number

lognormvariate()

function lognormvariate	(	local	mu,
		local	sigma
	)

Log normal distribution.

If you take the natural logarithm of this distribution, you'll get a

normal distribution with mean mu and standard deviation sigma.

mu can have any value, and sigma must be greater than zero.

Parameters

mu	number
sigma	number

Returns

number

normalvariate()

function normalvariate	(	local	mu,
		local	sigma
	)

Normal distribution.

mu is the mean, and sigma is the standard deviation.

Parameters

mu	number
sigma	number

Returns

number

paretovariate()

function paretovariate

(

local

alpha

)

Pareto distribution. alpha is the shape parameter.

Parameters

alpha

number

Returns

number

randint()

function randint	(	local	a,
		local	b
	)

Return random integer in range [a, b], including both end points.

Parameters

a	number
b	number

Returns

number

random()

function random

(

)

randrange()

function randrange	(	local	start,
		local	stop,
		local	step
	)

Choose a random item from range(start, stop[, step]).

It does not include the endpoint.

Parameters

start	number
stop	number
step	number

Returns

number

sample()

function sample	(	local	population,
		local	k
	)

Chooses k unique random elements from a population sequence or set.

Returns a new list containing elements from the population while

leaving the original population unchanged. The resulting list is

in selection order so that all sub-slices will also be valid random

samples. This allows raffle winners (the sample) to be partitioned

into grand prize and second place winners (the subslices).

Members of the population need not be hashable or unique. If the

population contains repeats, then each occurrence is a possible

selection in the sample.

Parameters

population	table
k	number

Returns

table

seed()

function seed

(

local

a

)

shuffle()

function shuffle	(	local	x,
		local	random
	)

Shuffle list x in place, and return None.

Optional argument random is a 0-argument function returning a

random float in [0.0, 1.0); if it is the default None, the

standard random.random will be used.

triangular()

function triangular	(	local	low,
		local	high,
		local	mode
	)

Triangular distribution.

Continuous distribution bounded by given lower and upper limits,

and having a given mode value in-between.

http://en.wikipedia.org/wiki/Triangular_distribution

Parameters

low	number
high	number
mode	number

Returns

number

uniform()

function uniform	(	local	a,
		local	b
	)

Get a random number in the range [a, b) or [a, b] depending on rounding.

Parameters

a	number
b	number

Returns

number

vonmisesvariate()

function vonmisesvariate	(	local	mu,
		local	kappa
	)

Circular data distribution.

mu is the mean angle, expressed in radians between 0 and 2*pi, and

kappa is the concentration parameter, which must be greater than or

equal to zero. If kappa is equal to zero, this distribution reduces

to a uniform random angle over the range 0 to 2*pi.

Parameters

mu	number
kappa	number

Returns

number

weibullvariate()

function weibullvariate	(	local	alpha,
		local	beta
	)

Weibull distribution.

alpha is the scale parameter and beta is the shape parameter.

Parameters

alpha	number
beta	number

Returns

number

---

The documentation for this class was generated from the following file:

• src/math/random.lua.hpp