View Source rand (stdlib v6.0)
Pseudo random number generation.
This module provides a pseudo random number generator. The module contains a number of algorithms. The uniform distribution algorithms are based on the Xoroshiro and Xorshift algorithms by Sebastiano Vigna. The normal distribution algorithm uses the Ziggurat Method by Marsaglia and Tsang on top of the uniform distribution algorithm.
For most algorithms, jump functions are provided for generating non-overlapping sequences for parallel computations. The jump functions perform calculations equivalent to perform a large number of repeated calls for calculating new states, but execute in a time roughly equivalent to one regular iteration per generator bit.
At the end of this module documentation there are also some niche algorithms to be used without this module's normal plug-in framework API that may be useful for special purposes like short generation time when quality is not essential, for seeding other generators, and such.
The following algorithms are provided:
exsss
(Since OTP 22.0)
Xorshift116**, 58 bits precision and period of 2^116-1Jump function: equivalent to 2^64 calls
This is the Xorshift116 generator combined with the StarStar scrambler from the 2018 paper by David Blackman and Sebastiano Vigna: Scrambled Linear Pseudorandom Number Generators
The generator does not need 58-bit rotates so it is faster than the Xoroshiro116 generator, and when combined with the StarStar scrambler it does not have any weak low bits like
exrop
(Xoroshiro116+).Alas, this combination is about 10% slower than
exrop
, but is despite that the default algorithm thanks to its statistical qualities.exro928ss
(Since OTP 22.0)
Xoroshiro928**, 58 bits precision and a period of 2^928-1Jump function: equivalent to 2^512 calls
This is a 58 bit version of Xoroshiro1024*, from the 2018 paper by David Blackman and Sebastiano Vigna: Scrambled Linear Pseudorandom Number Generators that on a 64 bit Erlang system executes only about 40% slower than the [default
exsss
algorithm* ](rand
)but with much longer period and better statistical properties, but on the flip side a larger state.Many thanks to Sebastiano Vigna for his help with the 58 bit adaption.
exrop
(Since OTP 20.0)
Xoroshiro116+, 58 bits precision and period of 2^116-1Jump function: equivalent to 2^64 calls
exs1024s
(Since OTP 20.0)
Xorshift1024*, 64 bits precision and a period of 2^1024-1Jump function: equivalent to 2^512 calls
exsp
(Since OTP 20.0)
Xorshift116+, 58 bits precision and period of 2^116-1Jump function: equivalent to 2^64 calls
This is a corrected version of the previous default algorithm, that now has been superseded by Xoroshiro116+ (
exrop
). Since there is no native 58 bit rotate instruction this algorithm executes a little (say < 15%) faster thanexrop
. See the algorithms' homepage.
The current default algorithm is
exsss
(Xorshift116**). If a specific algorithm is
required, ensure to always use seed/1
to initialize the state.
Which algorithm that is the default may change between Erlang/OTP releases, and is selected to be one with high speed, small state and "good enough" statistical properties.
Undocumented (old) algorithms are deprecated but still implemented so old code relying on them will produce the same pseudo random sequences as before.
Note
There were a number of problems in the implementation of the now undocumented algorithms, which is why they are deprecated. The new algorithms are a bit slower but do not have these problems:
Uniform integer ranges had a skew in the probability distribution that was not noticable for small ranges but for large ranges less than the generator's precision the probability to produce a low number could be twice the probability for a high.
Uniform integer ranges larger than or equal to the generator's precision used a floating point fallback that only calculated with 52 bits which is smaller than the requested range and therefore were not all numbers in the requested range even possible to produce.
Uniform floats had a non-uniform density so small values i.e less than 0.5 had got smaller intervals decreasing as the generated value approached 0.0 although still uniformly distributed for sufficiently large subranges. The new algorithms produces uniformly distributed floats on the form
N * 2.0^(-53)
hence equally spaced.
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 or be an explicit argument and return value.
The functions with implicit state use the process dictionary variable
rand_seed
to remember the current state.
If a process calls uniform/0
, uniform/1
or uniform_real/0
without setting
a seed first, seed/1
is called automatically with the
default algorithm and creates a non-constant
seed.
The functions with explicit state never use the process dictionary.
Examples:
Simple use; creates and seeds the default algorithm with a non-constant seed if not already done:
R0 = rand:uniform(),
R1 = rand:uniform(),
Use a specified algorithm:
_ = rand:seed(exs928ss),
R2 = rand:uniform(),
Use a specified algorithm with a constant seed:
_ = rand:seed(exs928ss, {123, 123534, 345345}),
R3 = rand:uniform(),
Use the functional API with a non-constant seed:
S0 = rand:seed_s(exsss),
{R4, S1} = rand:uniform_s(S0),
Textbook basic form Box-Muller standard normal deviate
R5 = rand:uniform_real(),
R6 = rand:uniform(),
SND0 = math:sqrt(-2 * math:log(R5)) * math:cos(math:pi() * R6)
Create a standard normal deviate:
{SND1, S2} = rand:normal_s(S1),
Create a normal deviate with mean -3 and variance 0.5:
{ND0, S3} = rand:normal_s(-3, 0.5, S2),
Note
The builtin random number generator algorithms are not cryptographically strong. If a cryptographically strong random number generator is needed, use something like
crypto:rand_seed/0
.
For all these generators except exro928ss
and exsss
the lowest bit(s) has
got a slightly less random behaviour than all other bits. 1 bit for exrop
(and
exsp
), and 3 bits for exs1024s
. See for example the explanation in the
Xoroshiro128+ generator
source code:
Beside passing BigCrush, this generator passes the PractRand test suite up to (and included) 16TB, with the exception of binary rank tests, which fail due to the lowest bit being an LFSR; all other bits pass all tests. We suggest to use a sign test to extract a random Boolean value.
If this is a problem; to generate a boolean with these algorithms use something like this:
(rand:uniform(256) > 128) % -> boolean()
((rand:uniform(256) - 1) bsr 7) % -> 0 | 1
For a general range, with N = 1
for exrop
, and N = 3
for exs1024s
:
(((rand:uniform(Range bsl N) - 1) bsr N) + 1)
The floating point generating functions in this module waste the lowest bits when converting from an integer so they avoid this snag.
Summary
Types
Algorithm specific internal state
Algorithm-dependent state that can be printed or saved to file.
Algorithm specific internal state
Algorithm specific internal state
Algorithm specific internal state
Algorithm specific internal state
Algorithm specific internal state
1 .. ((16#1ffb072 \* 2^29 - 1) - 1)
A seed value for the generator.
Algorithm specific state
Algorithm-dependent state.
0 .. (2^58 - 1)
0 .. (2^64 - 1)
Plug-in framework API
Returns, for a specified integer N >= 0
and a state, a binary/0
with that
number of random bytes, and a new state.
Returns the random number state in an external format. To be used with seed/1
.
Returns the random number generator state in an external format. To be used with
seed/1
.
Returns the state after performing jump calculation to the state in the process dictionary.
Returns the state after performing jump calculation to the given state.
Returns a standard normal deviate float (that is, the mean is 0 and the standard deviation is 1) and updates the state in the process dictionary.
Returns a normal N(Mean, Variance) deviate float and updates the state in the process dictionary.
Returns, for a specified state, a standard normal deviate float (that is, the mean is 0 and the standard deviation is 1) and a new state.
Returns, for a specified state, a normal N(Mean, Variance) deviate float and a new state.
Seeds random number generation with the specifed algorithm and time-dependent
data if AlgOrStateOrExpState
is an algorithm. Alg = default
is an alias for
the default algorithm.
Seeds random number generation with the specified algorithm and integers in the
process dictionary and returns the state. Alg = default
is an alias for the
default algorithm.
Seeds random number generation with the specifed algorithm and time-dependent
data if AlgOrStateOrExpState
is an algorithm. Alg = default
is an alias for
the default algorithm.
Seeds random number generation with the specified algorithm and integers and
returns the state. Alg = default
is an alias for the
default algorithm.
Returns a random float uniformly distributed in the value range 0.0 =< X < 1.0
and updates the state in the process dictionary.
Returns, for a specified integer N >= 1
, a random integer uniformly
distributed in the value range 1 =< X =< N
and updates the state in the
process dictionary.
Returns a random float uniformly distributed in the value range
DBL_MIN =< X < 1.0
and updates the state in the process dictionary.
Returns, for a specified state, a random float uniformly distributed in the
value range DBL_MIN =< X < 1.0
and updates the state in the process
dictionary.
Returns, for a specified state, random float uniformly distributed in the value
range 0.0 =< X < 1.0
and a new state.
Returns, for a specified integer N >= 1
and a state, a random integer
uniformly distributed in the value range 1 =< X =< N
and a new state.
Niche algorithms API
Returns a new generator state equivalent of the state after iterating over
exsp_next/1
2^64 times.
Returns a random 58-bit integer X
and a new generator state NewAlgState
,
according to the Xorshift116+ algorithm.
Returns a new generator state CX1
, according to a Multiply With Carry
generator, which is an efficient implementation of a Multiplicative Congruential
Generator with a power of 2 multiplier and a prime modulus.
Returns the generator value V
from a generator state CX
, as a float/0
.
The generator state is scrambled as with
mwc59_value/1
before converted to a float/0
.
Equivalent to mwc59_seed/1
Returns a generator state CX
. S
is hashed to create the generator state, to
avoid that similar seeds create similar sequences.
Returns a 32-bit value V
from a generator state CX
. The generator state is
scrambled using an 8-bit xorshift which masks the statistical imperfecions of
the base generator mwc59
enough to produce numbers of decent
quality. Still some problems in 2- and 3-dimensional birthday spacing and
collision tests show through.
Returns a 59-bit value V
from a generator state CX
. The generator state is
scrambled using an 4-bit followed by a 27-bit xorshift, which masks the
statistical imperfecions of the base generator mwc59
enough that
all 59 bits are of very good quality.
Returns a random 64-bit integer X
and a new generator state NewAlgState
,
according to the SplitMix64 algorithm.
Types
-type alg() :: builtin_alg() | atom().
-type alg_handler() :: #{type := alg(), bits => non_neg_integer(), weak_low_bits => non_neg_integer(), max => non_neg_integer(), next := fun((alg_state()) -> {non_neg_integer(), alg_state()}), uniform => fun((state()) -> {float(), state()}), uniform_n => fun((pos_integer(), state()) -> {pos_integer(), state()}), jump => fun((state()) -> state())}.
-type alg_state() :: exsplus_state() | exro928_state() | exrop_state() | exs1024_state() | exs64_state() | dummy_state() | term().
-type builtin_alg() :: exsss | exro928ss | exrop | exs1024s | exsp | exs64 | exsplus | exs1024 | dummy.
-type dummy_state() :: uint58().
Algorithm specific internal state
Algorithm-dependent state that can be printed or saved to file.
-opaque exro928_state()
Algorithm specific internal state
-opaque exrop_state()
Algorithm specific internal state
-opaque exs64_state()
Algorithm specific internal state
-opaque exs1024_state()
Algorithm specific internal state
-opaque exsplus_state()
Algorithm specific internal state
-type mwc59_state() :: 1..133850370 bsl 32 - 1 - 1.
1 .. ((16#1ffb072 \* 2^29 - 1) - 1)
A seed value for the generator.
A list of integers sets the generator's internal state directly, after algorithm-dependent checks of the value and masking to the proper word size. The number of integers must be equal to the number of state words in the generator.
An integer is used as the initial state for a SplitMix64 generator. The output values of that is then used for setting the generator's internal state after masking to the proper word size and if needed avoiding zero values.
A traditional 3-tuple of integers seed is passed through algorithm-dependent hashing functions to create the generator's initial state.
-type splitmix64_state() :: uint64().
Algorithm specific state
-type state() :: {alg_handler(), alg_state()}.
Algorithm-dependent state.
-type uint58() :: 0..1 bsl 58 - 1.
0 .. (2^58 - 1)
-type uint64() :: 0..1 bsl 64 - 1.
0 .. (2^64 - 1)
Plug-in framework API
-spec bytes(N :: non_neg_integer()) -> Bytes :: binary().
Returns, for a specified integer N >= 0
, a binary/0
with that number of
random bytes.
Generates as many random numbers as required using the selected algorithm to compose the binary, and updates the state in the process dictionary accordingly.
-spec bytes_s(N :: non_neg_integer(), State :: state()) -> {Bytes :: binary(), NewState :: state()}.
Returns, for a specified integer N >= 0
and a state, a binary/0
with that
number of random bytes, and a new state.
Generates as many random numbers as required using the selected algorithm to compose the binary, and the new state.
-spec export_seed() -> undefined | export_state().
Returns the random number state in an external format. To be used with seed/1
.
-spec export_seed_s(State :: state()) -> export_state().
Returns the random number generator state in an external format. To be used with
seed/1
.
-spec jump() -> NewState :: state().
Returns the state after performing jump calculation to the state in the process dictionary.
This function generates a not_implemented
error exception when the jump
function is not implemented for the algorithm specified in the state in the
process dictionary.
Returns the state after performing jump calculation to the given state.
This function generates a not_implemented
error exception when the jump
function is not implemented for the algorithm specified in the state.
-spec normal() -> float().
Returns a standard normal deviate float (that is, the mean is 0 and the standard deviation is 1) and updates the state in the process dictionary.
Returns a normal N(Mean, Variance) deviate float and updates the state in the process dictionary.
Returns, for a specified state, a standard normal deviate float (that is, the mean is 0 and the standard deviation is 1) and a new state.
Returns, for a specified state, a normal N(Mean, Variance) deviate float and a new state.
-spec seed(AlgOrStateOrExpState :: builtin_alg() | state() | export_state()) -> state(); (Alg :: default) -> state().
Seeds random number generation with the specifed algorithm and time-dependent
data if AlgOrStateOrExpState
is an algorithm. Alg = default
is an alias for
the default algorithm.
Otherwise recreates the exported seed in the process dictionary, and returns the
state. See also export_seed/0
.
-spec seed(Alg :: builtin_alg(), Seed :: seed()) -> state(); (Alg :: default, Seed :: seed()) -> state().
Seeds random number generation with the specified algorithm and integers in the
process dictionary and returns the state. Alg = default
is an alias for the
default algorithm.
-spec seed_s(AlgOrStateOrExpState :: builtin_alg() | state() | export_state()) -> state(); (Alg :: default) -> state().
Seeds random number generation with the specifed algorithm and time-dependent
data if AlgOrStateOrExpState
is an algorithm. Alg = default
is an alias for
the default algorithm.
Otherwise recreates the exported seed and returns the state. See also
export_seed/0
.
-spec seed_s(Alg :: builtin_alg(), Seed :: seed()) -> state(); (Alg :: default, Seed :: seed()) -> state().
Seeds random number generation with the specified algorithm and integers and
returns the state. Alg = default
is an alias for the
default algorithm.
-spec uniform() -> X :: float().
Returns a random float uniformly distributed in the value range 0.0 =< X < 1.0
and updates the state in the process dictionary.
The generated numbers are on the form N * 2.0^(-53)
, that is; equally spaced in
the interval.
Warning
This function may return exactly
0.0
which can be fatal for certain applications. If that is undesired you can use(1.0 - rand:uniform())
to get the interval0.0 < X =< 1.0
, or instead useuniform_real/0
.If neither endpoint is desired you can test and re-try like this:
my_uniform() -> case rand:uniform() of 0.0 -> my_uniform(); X -> X end end.
-spec uniform(N :: pos_integer()) -> X :: pos_integer().
Returns, for a specified integer N >= 1
, a random integer uniformly
distributed in the value range 1 =< X =< N
and updates the state in the
process dictionary.
-spec uniform_real() -> X :: float().
Returns a random float uniformly distributed in the value range
DBL_MIN =< X < 1.0
and updates the state in the process dictionary.
Conceptually, a random real number R
is generated from the interval
0 =< R < 1
and then the closest rounded down normalized number in the IEEE 754
Double precision format is returned.
Note
The generated numbers from this function has got better granularity for small numbers than the regular
uniform/0
because all bits in the mantissa are random. This property, in combination with the fact that exactly zero is never returned is useful for algorithms doing for example1.0 / X
ormath:log(X)
.
See uniform_real_s/1
for more explanation.
Returns, for a specified state, a random float uniformly distributed in the
value range DBL_MIN =< X < 1.0
and updates the state in the process
dictionary.
Conceptually, a random real number R
is generated from the interval
0 =< R < 1
and then the closest rounded down normalized number in the IEEE 754
Double precision format is returned.
Note
The generated numbers from this function has got better granularity for small numbers than the regular
uniform_s/1
because all bits in the mantissa are random. This property, in combination with the fact that exactly zero is never returned is useful for algorithms doing for example1.0 / X
ormath:log(X)
.
The concept implicates that the probability to get exactly zero is extremely
low; so low that this function is in fact guaranteed to never return zero. The
smallest number that it might return is DBL_MIN
, which is 2.0^(-1022)
.
The value range stated at the top of this function description is technically
correct, but 0.0 =< X < 1.0
is a better description of the generated numbers'
statistical distribution. Except that exactly 0.0 is never returned, which is
not possible to observe statistically.
For example; for all sub ranges N*2.0^(-53) =< X < (N+1)*2.0^(-53)
where
0 =< integer(N) < 2.0^53
the probability is the same. Compare that with the
form of the numbers generated by uniform_s/1
.
Having to generate extra random bits for small numbers costs a little
performance. This function is about 20% slower than the regular uniform_s/1
Returns, for a specified state, random float uniformly distributed in the value
range 0.0 =< X < 1.0
and a new state.
The generated numbers are on the form N * 2.0^(-53)
, that is; equally spaced in
the interval.
Warning
This function may return exactly
0.0
which can be fatal for certain applications. If that is undesired you can use(1.0 - rand:uniform(State))
to get the interval0.0 < X =< 1.0
, or instead useuniform_real_s/1
.If neither endpoint is desired you can test and re-try like this:
my_uniform(State) -> case rand:uniform(State) of {0.0, NewState} -> my_uniform(NewState); Result -> Result end end.
-spec uniform_s(N :: pos_integer(), State :: state()) -> {X :: pos_integer(), NewState :: state()}.
Returns, for a specified integer N >= 1
and a state, a random integer
uniformly distributed in the value range 1 =< X =< N
and a new state.
Niche algorithms API
-spec exsp_jump(AlgState :: exsplus_state()) -> NewAlgState :: exsplus_state().
Returns a new generator state equivalent of the state after iterating over
exsp_next/1
2^64 times.
See the description of jump functions at the top of this module description.
-spec exsp_next(AlgState :: exsplus_state()) -> {X :: uint58(), NewAlgState :: exsplus_state()}.
Returns a random 58-bit integer X
and a new generator state NewAlgState
,
according to the Xorshift116+ algorithm.
This is an API function into the internal implementation of the
exsp
algorithm that enables using it without the
overhead of the plug-in framework, which might be useful for time critial
applications. On a typical 64 bit Erlang VM this approach executes in just above
30% (1/3) of the time for the default algorithm through this module's normal
plug-in framework.
To seed this generator use {_, AlgState} = rand:seed_s(exsp)
or
{_, AlgState} = rand:seed_s(exsp, Seed)
with a specific Seed
.
Note
This function offers no help in generating a number on a selected range, nor in generating a floating point number. It is easy to accidentally mess up the fairly good statistical properties of this generator when doing either. See the recepies at the start of this Niche algorithms API description. Note also the caveat about weak low bits that this generator suffers from. The generator is exported in this form primarily for performance.
-spec mwc59(CX0 :: mwc59_state()) -> CX1 :: mwc59_state().
Returns a new generator state CX1
, according to a Multiply With Carry
generator, which is an efficient implementation of a Multiplicative Congruential
Generator with a power of 2 multiplier and a prime modulus.
This generator uses the multiplier 2^32
and the modulus 16#7fa6502 * 2^32 - 1
,
which have been selected, in collaboration with Sebastiano Vigna, to avoid
bignum operations and still get good statistical quality. It can be written
as:
C = CX0 bsr 32
X = CX0 band ((1 bsl 32)-1))
CX1 = 16#7fa6502 * X + C
Because the generator uses a multiplier that is a power of 2 it gets statistical flaws for collision tests and birthday spacings tests in 2 and 3 dimensions, and even these caveats apply only to the MWC "digit", that is the low 32 bits (due to the multiplier) of the generator state.
The quality of the output value improves much by using a scrambler instead of
just taking the low bits. Function mwc59_value32
is a
fast scrambler that returns a decent 32-bit number. The slightly slower
mwc59_value
scrambler returns 59 bits of very good quality,
and mwc59_float
returns a float/0
of very good quality.
The low bits of the base generator are surprisingly good, so the lowest 16 bits
actually pass fairly strict PRNG tests, despite the generator's weaknesses that
lie in the high bits of the 32-bit MWC "digit". It is recommended to use rem
on the the generator state, or bit mask extracting the lowest bits to produce
numbers in a range 16 bits or less. See the recepies at the start of this
Niche algorithms API description.
On a typical 64 bit Erlang VM this generator executes in below 8% (1/13) of the
time for the default algorithm in the
plug-in framework API of this module. With the
mwc59_value32
scrambler the total time becomes 16% (1/6),
and with mwc59_value
it becomes 20% (1/5) of the time for
the default algorithm. With mwc59_float
the total time is
60% of the time for the default algorithm generating a float/0
.
Note
This generator is a niche generator for high speed applications. It has a much shorter period than the default generator, which in itself is a quality concern, although when used with the value scramblers it passes strict PRNG tests. The generator is much faster than
exsp_next/1
but with a bit lower quality.
-spec mwc59_float(CX :: mwc59_state()) -> V :: float().
Returns the generator value V
from a generator state CX
, as a float/0
.
The generator state is scrambled as with
mwc59_value/1
before converted to a float/0
.
-spec mwc59_seed() -> CX :: mwc59_state().
Equivalent to mwc59_seed/1
-spec mwc59_seed(S :: 0..1 bsl 58 - 1) -> CX :: mwc59_state().
Returns a generator state CX
. S
is hashed to create the generator state, to
avoid that similar seeds create similar sequences.
Without S
, the generator state is created as for
seed_s(atom())
.
-spec mwc59_value32(CX :: mwc59_state()) -> V :: 0..1 bsl 32 - 1.
Returns a 32-bit value V
from a generator state CX
. The generator state is
scrambled using an 8-bit xorshift which masks the statistical imperfecions of
the base generator mwc59
enough to produce numbers of decent
quality. Still some problems in 2- and 3-dimensional birthday spacing and
collision tests show through.
When using this scrambler it is in general better to use the high bits of the value than the low. The lowest 8 bits are of good quality and pass right through from the base generator. They are combined with the next 8 in the xorshift making the low 16 good quality, but in the range 16..31 bits there are weaker bits that you do not want to have as the high bits of your generated values. Therefore it is in general safer to shift out low bits. See the recepies at the start of this Niche algorithms API description.
For a non power of 2 range less than about 16 bits (to not get too much bias and
to avoid bignums) truncated multiplication can be used, which is much faster
than using rem
: (Range*V) bsr 32
.
-spec mwc59_value(CX :: mwc59_state()) -> V :: 0..1 bsl 59 - 1.
Returns a 59-bit value V
from a generator state CX
. The generator state is
scrambled using an 4-bit followed by a 27-bit xorshift, which masks the
statistical imperfecions of the base generator mwc59
enough that
all 59 bits are of very good quality.
Be careful to not accidentaly create a bignum when handling the value V
.
It is in general general better to use the high bits from this scrambler than the low. See the recepies at the start of this Niche algorithms API description.
For a non power of 2 range less than about 29 bits (to not get too much bias and
to avoid bignums) truncated multiplication can be used, which is much faster
than using rem
. Example for range 1'000'000'000; the range is 30 bits, we use
29 bits from the generator, adding up to 59 bits, which is not a bignum:
(1000000000 * (V bsr (59-29))) bsr 29
.
-spec splitmix64_next(AlgState :: integer()) -> {X :: uint64(), NewAlgState :: splitmix64_state()}.
Returns a random 64-bit integer X
and a new generator state NewAlgState
,
according to the SplitMix64 algorithm.
This generator is used internally in the rand
module for seeding other
generators since it is of a quite different breed which reduces the probability
for creating an accidentally bad seed.