# random

## Pseudo random number generation

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 current 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 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.

It should be noted that this random number generator is not cryptographically
strong. If a strong cryptographic random number generator is needed for
example `crypto:rand_bytes/1`

could be used instead.

## Note!

The new and improved rand module should be used instead of this module.

#### Functions

### seed() -> ran()

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

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

`A1 = A2 = A3 = integer()`

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

One easy way of obtaining a unique value to seed with is to:

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

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

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

`A1 = A2 = A3 = integer()`

`seed({`

is equivalent to `seed(`

.

### 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

`N = integer() >= 1`

Given an integer

, `uniform/1`

returns a
random integer uniformly distributed between `1`

and

, updating the state in the process dictionary.

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

`State0 = State1 = ran()`

Given a state, `uniform_s/1`

returns a random float uniformly
distributed between `0.0`

and `1.0`

, and a new state.

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

`N = integer() >= 1`

`State0 = State1 = ran()`

Given an integer

and a state, `uniform_s/2`

returns a random integer uniformly distributed between `1`

and

, 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 R15. Upgrading to R15 will break
applications that expect a specific output for a given seed. The output
is still deterministic number series, but different compared to releases
older than R15. The seed `{0,0,0}`

will for example no longer
produce a flawed series of only zeros.