## NAME

random − Pseudo-random number generation.

## DESCRIPTION

This module provides a random number generator. The method is attributed to B.A. Wichmann and I.D. Hill in ’An efficient and portable pseudo-random number generator’, Journal of Applied Statistics. AS183. 1982. Also Byte March 1987.

The algorithm is a modification of the version attributed to Richard A. O’Keefe in the standard Prolog library.

Every time a
random number is requested, a state is used to calculate it,
and a new state is produced. The state can either be
implicit (kept in the process dictionary) or be an explicit
argument and return value. In this implementation, the state
(the type *ran()*) consists of a tuple of three
integers.

**Note:**

This random number generator is
not cryptographically strong. If a strong cryptographic
random number generator is needed, use one of functions in
the *crypto* module, for example,
*crypto:strong_rand_bytes/1*.

**Note:**

The improved *rand* module
is to be used instead of this module.

## DATA TYPES

**ran()** =
{integer(), integer(), integer()}

The state.

## EXPORTS

**seed() ->
ran()**

Seeds random number generation with default (fixed) values in the process dictionary and returns the old state.

**seed(SValue)
-> undefined | ran()**

Types:

SValue = {A1,
A2, A3} | integer()

A1 = A2 = A3 = integer()

*seed({A1,
A2, A3})* is equivalent to *seed(A1, A2, A3)*.

**seed(A1, A2,
A3) -> undefined | ran()**

Types:

A1 = A2 = A3 = integer()

Seeds random number generation with integer values in the process dictionary and returns the old state.

The following is an easy way of obtaining a unique value to seed with:

random:seed(erlang:phash2([node()]),

erlang:monotonic_time(),

erlang:unique_integer())

For details,
see *erlang:phash2/1*, *erlang:node/0*,
*erlang:monotonic_time/0*, and
*erlang:unique_integer/0*.

**seed0()
-> ran()**

Returns the default state.

**uniform()
-> float()**

Returns a
random float uniformly distributed between *0.0* and
*1.0*, updating the state in the process
dictionary.

**uniform(N)
-> integer() >= 1**

Types:

N = integer() >= 1

Returns, for a
specified integer *N >= 1*, a random integer
uniformly distributed between *1* and *N*,
updating the state in the process dictionary.

**uniform_s(State0)
-> {float(), State1}**

Types:

State0 = State1
= **ran()**

Returns, for a
specified state, a random float uniformly distributed
between *0.0* and *1.0*, and a new state.

**uniform_s(N,
State0) -> {integer(), State1}**

Types:

N = integer()
>= 1

State0 = State1 = **ran()**

Returns, for a
specified integer *N >= 1* and a state, a random
integer uniformly distributed between *1* and *N*,
and a new state.

## NOTE

Some of the
functions use the process dictionary variable
*random_seed* to remember the current seed.

If a process
calls *uniform/0* or *uniform/1* without setting a
seed first, *seed/0* is called automatically.

The
implementation changed in Erlang/OTP R15. Upgrading to R15
breaks applications that expect a specific output for a
specified seed. The output is still deterministic number
series, but different compared to releases older than R15.
Seed *{0,0,0}* does, for example, no longer produce a
flawed series of only zeros.