rand(3)

NAME
rand - Pseudo random number generation

DESCRIPTION

This module provides Pseudo Random Number Generation and implements a number of base generator algorithms. Most are provided through a plug-in framework that adds essential features to the base generators.

PRNGs in general, and so the algorithms in this module, are mostly used for test and simulation. They are designed for good statistical quality and high generation speed.

A generator algorithm, for each iteration, takes a state as input and produces a raw pseudo random number and a new state to be used for the next iteration.

A particular state always produces the same number and new state. The initial state is produced from a seed. This makes it possible to repeat for example a simulation with the same random number sequence, by re-using the same seed. There are also the functions export_seed/0 and export_seed_s/1 that capture the PRNG state in an export_state/0, that can be used to start from a known state.

This property, and others, make the algorithms in this module unsuitable for cryptographical applications, but in the crypto module there are suitable generators, for this module's plug-in framework. See crypto:rand_seed_s/0 and crypto:rand_seed_alg_s/1.

At the end of this module documentation there are some niche algorithms that do not use this module's normal plug-in framework. They are useful for special purposes like fast generation when quality is not essential, for seeding other generators, and such.

  Plug-in framework

The raw pseudo random numbers produced by the base generators are only appropriate in some cases such as power of two ranges less than the generator size, and some have quirks, for example weak low bits. Therefore, the Plug-in Framework implements a common API for all base generators, that add essential or useful funcionality:

    • Keeping the generator state in the process dictionary.

    • Automatic seeding.

    • Seeding support for manual seeding to avoid common pitfalls.

    • Generating integers with uniform distribution, in any range, without bias. The range is not limited; it may be larger than the base generator's size (but that costs some performance).

    • Generating floating-point numbers with uniform distribution.

    • Generating floating-point numbers with normal distribution, standard normal distribution or specified mean and variance.

    • Generating any number of bytes.

    • Jumping the generator ahead, in algorithms that support that.

Usage and examples

A generator has to be initialized. This is done by one of the seed/1 or seed_s/1 functions, which also select which algorithm to use. The seed/1 functions store the generator and state in the process dictionary, while the seed_s/1 functions only return the state, which requires the calling code to handle the state and updates to it.

The seed functions that do not have a Seed value as an argument create an automatic seed that should be unique to the created generator instance; see seed_s/1.

If an automatic seed is not desired, the seed functions that have a Seed argument can be used. The argument has 3 possible formats; see the seed/0 type description.

Plug-in framework API functions named with the suffix _s take an explicit state as the last argument and return the new state as the last element in the returned tuple. The process dictionary is not used.

Sibling functions without that suffix take an implicit state from and store the new state in the process dictionary, and only return their "interesting " output value. If the process dictionary does not contain a state, seed(default) is implicitly called to create an automatic seed for the default algorithm as initial state.

Usage

First initialize a generator by calling one of the seed functions, which also selects a PRNG algorithm.

Then call a Plug-in framework API function either with an explicit state from the seed function and use the returned new state in the next call, or call an API function without an explicit state argument to operate on the state in the process dictionary.

Examples

    %% Generate two uniformly distibuted floating point numbers.
    %%
    %% By not calling a [seed](`seed/1`) function, this uses
    %% the generator state and algorithm in the process dictionary.
    %% If there is no state there, [`seed(default)`](`seed/1`)
    %% is implicitly called first:
    %%
    1> R0 = rand:uniform(),
       is_float(R0) andalso 0.0 =< R0 andalso R0 < 1.0.
    true
    2> R1 = rand:uniform(),
       is_float(R1) andalso 0.0 =< R1 andalso R1 < 1.0.
    true
    %% Generate a uniformly distributed integer in the range 1..4711:
    %%
    3> K0 = rand:uniform(4711),
       is_integer(K0) andalso 1 =< K0 andalso K0 =< 4711.
    true
    %% Generate a binary with 16 bytes, uniformly distributed:
    %%
    4> B0 = rand:bytes(16),
       byte_size(B0) == 16.
    true
    %% Select and initialize a specified algorithm,
    %% with an automatic default seed, then generate
    %% a floating point number:
    %%
    5> rand:seed(exro928ss).
    6> R2 = rand:uniform(),
       is_float(R2) andalso 0.0 =< R2 andalso R2 < 1.0.
    true
    %% Select and initialize a specified algorithm
    %% with a specified seed, then generate
    %% a floating point number:
    %%
    7> rand:seed(exro928ss, 123456789).
    8> R3 = rand:uniform().
    0.48303622772415256
    %% Select and initialize a specific algorithm,
    %% with an automatic default seed, using the functional API
    %% with explicit generator state, then generate
    %% two floating point numbers.
    %%
    9>  S0 = rand:seed_s(exsss).
    10> {R4, S1} = rand:uniform_s(S0),
        is_float(R4) andalso 0.0 =< R4 andalso R4 < 1.0.
    true
    11> {R5, S2} = rand:uniform_s(S1),
        is_float(R5) andalso 0.0 =< R5 andalso R5 < 1.0.
    true
    %% Repeat the first after seed
    12> {R4, _} = rand:uniform_s(S0).
    %% Generate a standard normal distribution number
    %% using the built-in fast Ziggurat Method:
    %%
    13> {SND0, S3} = rand:normal_s(S2),
        is_float(SND0).
    true
    %% Generate a normal distribution number
    %% with mean -3 and variance 0.5:
    %%
    14> {ND0, S4} = rand:normal_s(-3, 0.5, S3),
        is_float(ND0).
    true
    %% Generate a textbook basic form Box-Muller
    %% standard normal distribution number, which has the same
    %% distribution as the built-in Ziggurat method above,
    %% but is much slower:
    %%
    15> R6 = rand:uniform_real(),
        is_float(R6) andalso 0.0 < R6 andalso R6 < 1.0.
    true
    16> R7 = rand:uniform(),
        is_float(R7) andalso 0.0 =< R7 andalso R7 < 1.0.
    true
    %% R6 cannot be equal to 0.0 so math:log/1 will never fail
    17> SND1 = math:sqrt(-2 * math:log(R6)) * math:cos(math:pi() * R7).

  Algorithms

The base generator algorithms implement the Xoroshiro and Xorshift algorithms by Sebastiano Vigna. During an iteration they generate an integer (at least 58-bit) and operate on a state of several integers. The size of these integers is chosen to not require bignum arithmetic on 64-bit platforms, which facilitates fast integer operations, in particular when handled by the JIT VM.

For most algorithms, jump functions are provided for generating non-overlapping sequences. A jump function perform a calculation equivalent to a large number of repeated state iterations, but execute in a time roughly equivalent to one regular iteration per generator bit.

By using a jump function instead of starting several generators from different seeds it is assured that the generated sequences do not overlap. The alternative of using different seeds may accidentally start the generators in sequence positions that are close to each other, but a jump function jumps to a sequence position very far ahead.

To create numbers with normal distribution the Ziggurat Method by Marsaglia and Tsang is used on the output from a base generator.

The following algorithms are provided:

    • exsss, the default algorithm (Since OTP 22.0) Xorshift116**, 58 bits precision and period of 2^116-1.

    Jump 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 use 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 despite that it is the default algorithm thanks to its statistical qualities.

    • exro928ss (Since OTP 22.0) Xoroshiro928**, 58 bits precision and a period of 2^928-1.

    Jump 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 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 2^116-1.

    Jump function: equivalent to 2^64 calls.

    • exs1024s (Since OTP 20.0) Xorshift1024*, 64 bits precision and a period of 2^1024-1

    Jump function: equivalent to 2^512 calls.

    Since this generator operates on 64-bit integers that are bignums on 64 bit platforms, it is much slower than exro928ss above.

    • exsp (Since OTP 20.0) Xorshift116+, 58 bits precision and period of 2^116-1

    Jump function: equivalent to 2^64 calls.

    This is a corrected version of a previous default algorithm (exsplus, deprecated), that was superseded by Xoroshiro116+ (exrop). Since this algorithm does not use rotate operations it executes a little (say < 15%) faster than exrop (that has to do a 58 bit rotate, for which there is no native instruction). See the algorithms' homepage.

Default Algorithm

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. So to ensure that the same sequence is reproduced on a later Erlang/OTP release, use a seed/2 or seed_s/2 to select both a specific algorithm and the seed value.

Old Algorithms

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 all numbers in the requested range were not even possible to produce. Uniform floats had a non-uniform density so small values for example 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 of the form N * 2.0^(-53) hence they are equally spaced.

Quality of the Generated Numbers

NOTE: The builtin random number generator algorithms are not cryptographically strong. If a cryptographically strong random number generator is needed, use for example crypto:rand_seed_s/0 or crypto:rand_seed_alg_s/1.

For all these generators except exro928ss and exsss the lowest bit(s) have got a slightly less random behaviour than all other bits. 1 bit for exrop (and exsp), and 3 bits for exs1024s. See for example this 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.

  Niche algorithms

The niche algorithms API contains special purpose algorithms that do not use the plug-in framework, mainly for performance reasons.

Since these algorithms lack the plug-in framework support, generating numbers in a range other than the base generator's range may become a problem.

There are at least four ways to do this, assuming the Range is less than the generator's range:

    • Modulo To generate a number V in the range 0..Range-1:

        Generate a number X. Use V = X rem Range as your value.

    This method uses rem, that is, the remainder of an integer division, which is a slow operation.

    Low bits from the generator propagate straight through to the generated value, so if the generator has got weaknesses in the low bits this method propagates them too.

    If Range is not a divisor of the generator range, the generated numbers have a bias. Example:

    Say the generator generates a byte, that is, the generator range is 0..255, and the desired range is 0..99 (Range = 100). Then there are 3 generator outputs that produce the value 0, these are; 0, 100 and 200. But there are only 2 generator outputs that produce the value 99, which are; 99 and 199. So the probability for a value V in 0..55 is 3/2 times the probability for the other values 56..99.

    If Range is much smaller than the generator range, then this bias gets hard to detect. The rule of thumb is that if Range is smaller than the square root of the generator range, the bias is small enough. Example:

    A byte generator when Range = 20. There are 12 (256 div 20) possibilities to generate the highest numbers and one more to generate a number V < 16 (256 rem 20). So the probability is 13/12 for a low number versus a high. To detect that difference with some confidence you would need to generate a lot more numbers than the generator range, 256 in this small example.

    • Truncated multiplication To generate a number V in the range 0..Range-1, when you have a generator with a power of 2 range (0..2^Bits-1):

        Generate a number X. Use V = X * Range bsr Bits as your value.

    If the multiplication X * Range creates a bignum this method becomes very slow.

    High bits from the generator propagate through to the generated value, so if the generator has got weaknesses in the high bits this method propagates them too.

    If Range is not a divisor of the generator range, the generated numbers have a bias, pretty much as for the Modulo method above.

    • Shift or mask To generate a number in a power of 2 range (0..2^RBits-1), when you have a generator with a power of 2 range (0..2^Bits):

        Generate a number X. Use V = X band ((1 bsl RBits)-1) or V = X bsr (Bits-RBits) as your value.

    Masking with band preserves the low bits, and right shifting with bsr preserves the high, so if the generator has got weaknesses in high or low bits; choose the right operator.

    If the generator has got a range that is not a power of 2 and this method is used anyway, it introduces bias in the same way as for the Modulo method above.

    • Rejection

        Generate a number X. If X is in the range, use it as your value, otherwise reject it and repeat.

    In theory it is not certain that this method will ever complete, but in practice you ensure that the probability of rejection is low. Then the probability for yet another iteration decreases exponentially so the expected mean number of iterations will often be between 1 and 2. Also, since the base generator is a full length generator, a value that will break the loop must eventually be generated.

    These methods can be combined, such as using the Modulo method and only if the generator value would create bias use Rejection. Or using Shift or mask to reduce the size of a generator value so that Truncated multiplication will not create a bignum.

    The recommended way to generate a floating point number (IEEE 745 Double, that has got a 53-bit mantissa) in the range 0..1, that is 0.0 =< V < 1.0 is to generate a 53-bit number X and then use V = X * (1.0/((1 bsl 53))) as your value. This will create a value of the form N*2^-53 with equal probability for every possible N for the range.

DATA TYPES

alg() :: builtin_alg() | atom().
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())}.
alg_state() :: exsplus_state() | exro928_state() | exrop_state() | exs1024_state() | exs64_state() | dummy_state() | term().
builtin_alg() :: exsss | exro928ss | exrop | exs1024s | exsp | exs64 | exsplus | exs1024 | dummy.
dummy_state() :: uint58().

    Algorithm specific internal state
export_state() :: {alg(), alg_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 exs1024_state()

    Algorithm specific internal state
-opaque exs64_state()

    Algorithm specific internal state
-opaque exsplus_state()

    Algorithm specific internal state
mwc59_state() :: 1..133850370 bsl 32 - 1 - 1.

    1 .. (16#1ffb072 bsl 29) - 2
seed() :: [integer()] | integer() | {integer(), integer(), integer()}.

    Generator seed value.

    A single integer is the easiest to use. It is set as the initial state of a SplitMix64 generator. The sequential output values of that generator are then used for setting the actual generator's internal state, after masking to the proper word size and avoiding zero values, if necessary.

    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. This format would only be needed in special cases.

    A traditional 3-tuple of integers is passed through algorithm-dependent hashing functions to create the generator's initial state. This format is inherited from this module's predecessor, the random module, where the 3-tuple from erlang:now/0 (also now deprectated) was often used for seeding to get some uniqueness.
splitmix64_state() :: uint64().

    Algorithm specific state
state() :: {alg_handler(), alg_state()}.

    Algorithm-dependent state.
uint58() :: 0..1 bsl 58 - 1.

    0 .. (2^58 - 1)
uint64() :: 0..1 bsl 64 - 1.

    0 .. (2^64 - 1)

FUNCTIONS

bytes(N :: non_neg_integer()) -> Bytes :: binary().

    Generate random bytes as a binary(), using the state in the process dictionary.

    Like bytes_s/2 but operates on the state stored in the process dictionary. Returns the generated Bytes.
bytes_s(N :: non_neg_integer(), State :: state()) -> {Bytes :: binary(), NewState :: state()}.

    Generate random bytes as a binary().

    For a specified integer N >= 0, generates a binary/0 with that number of random bytes.

    The selected algorithm is used to generate as many random numbers as required to compose the binary/0. Returns the generated Bytes and a NewState.

        Note

        The crypto module contains a function crypto:strong_rand_bytes/1 that does the same thing, but cryptographically secure. It is pretty fast and efficient on modern systems. This function, however, offers the possibility to reproduce a byte sequence by re-using seed, which a cryptographically secure function cannot do. Alas, because this function is based on a PRNG that produces random integers, thus has to create bytes from integers, it becomes rather slow. Particularly inefficient and slow is to use a rand plug-in generator from crypto such as crypto:rand_seed_s/0 to call this function for generating bytes. Since in that case it is not possible to reproduce the byte sequence anyway; it is better to use crypto:strong_rand_bytes/1 directly.
export_seed() -> undefined | export_state().

    Export the seed value.

    Returns the random number state in an external format. To be used with seed/1.
export_seed_s(State :: state()) -> export_state().

    Export the seed value.

    Returns the random number generator state in an external format. To be used with seed/1.
exsp_jump(AlgState :: exsplus_state()) -> NewAlgState :: exsplus_state().

    Jump the generator state forward.

    Performs a State jump calculation that is equivalent to a 2^64 state iterations.

    Returns the NewState.

    This feature can be used to create many non-overlapping random number sequences from one start state.

    See the description of jump functions at the top of this module description.

    See exsp_next/1 about why this internal implementation function has been exposed.
exsp_next(AlgState :: exsplus_state()) -> {X :: uint58(), NewAlgState :: exsplus_state()}.

    Generate an Xorshift116+ random integer and new algorithm state.

    From the specified AlgState, generates a random 58-bit integer X and a new algorithm state NewAlgState, according to the Xorshift116+ algorithm.

    This is an API function exposing 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 floating point numbers. It is easy to accidentally mess up the statistical properties of this generator or to loose the performance advantage when doing either. See the recipes 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 reasons.
jump() -> NewState :: state().

    Jump the generator state forward.

    Like jump/1 but operates on the state stored in the process dictionary. Returns the NewState.
jump(State :: state()) -> NewState :: state().

    Jump the generator state forward.

    Performs an algorithm specific State jump calculation that is equivalent to a large number of state iterations. See this module's algorithms list.

    Returns the NewState.

    This feature can be used to create many non-overlapping random number sequences from one start state; see the start of section Algorithms describing jump functions.

    This function raises a not_implemented error exception if there is no jump function implemented for the State's algorithm.
mwc59(CX0 :: mwc59_state()) -> CX1 :: mwc59_state().

    Generate a new MWC59 state.

    From the specified generator state CX0 generate a new 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 has been named "MWC59" and 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 these caveats apply even when looking only at the MWC "digit", that is the low 32 bits (the multiplier) of the generator state. The higher bits of the state are worse.

    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 recipes 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 and much shorter period.
mwc59_float(CX :: mwc59_state()) -> V :: float().

    Calculate a scrambled float/0 from a MWC59 state.

    Returns a value V :: float/0 from a generator state CX, in the range 0.0 =< V < 1.0 like for example uniform_s/1.

    The generator state is scrambled as with mwc59_value/1 before converted to a float/0.
mwc59_seed() -> CX :: mwc59_state().

    Create a MWC59 generator state.

    Like mwc59_seed/1 but it hashes the default seed value of seed_s(atom()).
mwc59_seed(S :: 0..1 bsl 58 - 1) -> CX :: mwc59_state().

    Create a MWC59 generator state.

    Returns a generator state CX. The 58-bit seed value S is hashed to create the generator state, to avoid that similar seeds create similar sequences.
mwc59_value(CX :: mwc59_state()) -> V :: 0..1 bsl 59 - 1.

    Calculate a 59-bit scrambled value from a MWC59 state.

    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 MWC59 base generator 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 recipes 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 (on a 64-bit VM ): (1000000000 * (V bsr (59-29))) bsr 29.
mwc59_value32(CX :: mwc59_state()) -> V :: 0..1 bsl 32 - 1.

    Calculate a 32-bit scrambled value from a MWC59 state.

    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 are passed 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 should not become high bits of the generated values.

    Therefore it is in general safer to shift out low bits. See the recipes 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, that is: (Range*V) bsr 32, which is much faster than using rem.
normal() -> X :: float().

    Generate a random number with standard normal distribution.

    Like normal_s/1 but operates on the state stored in the process dictionary. Returns the generated number X.
normal(Mean :: number(), Variance :: number()) -> X :: float().

    Generate a random number with specified normal distribution 𝒩 (μ, σ²).

    Like normal_s/3 but operates on the state stored in the process dictionary. Returns the generated number X.
normal_s(State :: state()) -> {X :: float(), NewState :: state()}.

    Generate a random number with standard normal distribution.

    From the specified State, generates a random number X :: float/0, with standard normal distribution, that is with mean value 0.0 and variance 1.0.

    Returns the generated number X and the NewState.
normal_s(Mean, Variance, State) -> {X :: float(), NewState :: state()} when Mean :: number(), Variance :: number(), State :: state().

    Generate a random number with specified normal distribution 𝒩 (μ, σ²).

    From the specified State, generates a random number X :: float/0, with normal distribution 𝒩 (μ, σ²), that is 𝒩 (Mean, Variance) where Variance >= 0.0.

    Returns X and the NewState.
seed(Alg | State) -> state() when Alg :: builtin_alg() | default, State :: state() | export_state().

    Seed the random number generator and select algorithm.

    The same as seed_s(Alg_or_State), but also stores the generated state in the process dictionary.

    The argument default is an alias for the default algorithm that has been implemented (Since OTP 24.0).
seed(Alg, Seed) -> state() when Alg :: builtin_alg() | default, Seed :: seed().

    Seed the random number generator and select algorithm.

    The same as seed_s(Alg, Seed), but also stores the generated state in the process dictionary.

    Alg = default is an alias for the default algorithm that has been implemented (Since OTP 24.0).
seed_s(Alg | State) -> state() when Alg :: builtin_alg() | default, State :: state() | export_state().

    Seed the random number generator and select algorithm.

    With the argument Alg, select that algorithm and seed random number generation with reasonably unpredictable time dependent data that should be unique to the created generator instance. It is (for now) based on the node name, the calling pid/0, the system time, and a system unique integer. This set of fairly unique items may change in the future, if necessary.

    Alg = default is an alias for the default algorithm (Since OTP 24.0).

    With the argument State, re-creates the state and returns it. See also export_seed/0.
seed_s(Alg, Seed) -> state() when Alg :: builtin_alg() | default, Seed :: seed().

    Seed the random number generator and select algorithm.

    Creates and returns a generator state for the specified algorithm from the specified seed/0 integers.

    Alg = default is an alias for the default algorithm that has been implemented since OTP 24.0.
splitmix64_next(AlgState :: integer()) -> {X :: uint64(), NewAlgState :: splitmix64_state()}.

    Generate a SplitMix64 random 64-bit integer and new algorithm state.

    From the specified AlgState generates 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.
uniform() -> X :: float().

    Generate a uniformly distributed random number 0.0 =< X < 1.0, using the state in the process dictionary.

    Like uniform_s/1 but operates on the state stored in the process dictionary. Returns the generated number X.
uniform(N :: pos_integer()) -> X :: pos_integer().

    Generate a uniformly distributed random integer 1 =< X =< N, using the state in the process dictionary.

    Like uniform_s/2 but operates on the state stored in the process dictionary. Returns the generated number X.
uniform_real() -> X :: float().

    Generate a uniformly distributed random number 0.0 < X < 1.0, using the state in the process dictionary.

    Like uniform_real_s/1 but operates on the state stored in the process dictionary. Returns the generated number X.

    See uniform_real_s/1.
uniform_real_s(State :: state()) -> {X :: float(), NewState :: state()}.

    Generate a uniformly distributed random number 0.0 < X < 1.0.

    From the specified state, generates a random float, uniformly distributed in the value range DBL_MIN =< X < 1.0.

    Conceptually, a random real number R is generated from the interval 0.0 =< R < 1.0 and then the closest rounded down nonzero 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 example 1.0 / X or math:log(X).

    The concept implicates that the probability to get exactly zero is extremely low; so low that this function in fact never returns 0.0. The smallest number that it might return is DBL_MIN, which is 2.0^(-1022). However, the generators in this module have technical limitations on how many zero words in a row they can return, which limits the number of leading zeros that can be generated, which sets an upper limit for the smallest generated number, that is still extremely small.

    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. That this function never returns exactly 0.0 is impossible to observe.

    For all sub ranges N*2.0^(-53) =< X < (N+1)*2.0^(-53) where 0 =< integer(N) < 2.0^53, the probability to generate a number in a sub range is the same, very much like the numbers generated by uniform_s/1.

    Having to generate extra random bits for occasional small numbers costs a little performance. This function is about 20% slower than the regular uniform_s/1
uniform_s(State :: state()) -> {X :: float(), NewState :: state()}.

    Generate a uniformly distributed random number 0.0 =< X < 1.0.

    From the specified State, generates a random number X :: float/0, uniformly distributed in the value range 0.0 =< X < 1.0. Returns the number X and the updated NewState.

    The generated numbers are of 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 interval 0.0 < X =< 1.0, or instead use uniform_real/0. If neither endpoint is desired you can achieve the range 0.0 < X < 1.0 using test and re-try like this:

        my_uniform() ->
            case rand:uniform() of
                X when 0.0 < X -> X;
                _ -> my_uniform()
            end.
uniform_s(N :: pos_integer(), State :: state()) -> {X :: pos_integer(), NewState :: state()}.

    Generate a uniformly distributed random integer 1 =< X =< N.

    From the specified State, generates a random number X :: integer/0, uniformly distributed in the specified range 1 =< X =< N. Returns the number X and the updated NewState.