17 releases (3 stable)
Uses new Rust 2021
new 1.0.2  Aug 7, 2022 

1.0.1  Jul 14, 2022 
0.3.4  Jun 26, 2022 
0.3.3  May 29, 2022 
0.1.4  Feb 10, 2022 
#49 in Testing
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Used in 5 crates
(4 directly)
39KB
403 lines
Ran
Description
The rationale for this crate is to generate good quality random numbers fast, simply and with a minimal footprint.
Not everyone wants to add 375 kB, plus another ten dependencies, just to generate a bunch of random numbers for testing ( looking at the 'other' crate: rand
).
In contradistinction, this crate is lightweight and it has no dependencies.
Even so, there are four different algorithms on offer, plus a good range of utility functions to easily generate individual numbers of various types, vectors, and vectors of vectors filled with random numbers.
The main objective has been the ease of use rather than flatout speed but the algorithms are neverheless very fast.
It is highly recommended to run tests/tests.rs
with examples of usage.
Getting Started
use ran::*; or
use ran::{set_seeds,Rnum,Rv,Rvv};
These algorithms use thread safe static seeds. It is strongly recommended to initialise them with set_seeds(value);
in every thread where you may want to be generating random numbers, otherwise you will get the same sequence every time, based on the default value. Any u64 value will do to initiate a new, different, random sequence. Of course, the same seed will always produce the same sequence and this is sometimes actually useful for exact testing comparisons.
/// This function initialises SEED and xoshi seeds X0X3.
/// The supplied value must be non zero,
/// otherwise seeds will remain unchanged.
pub fn set_seeds( seed:u64 )
Generic Usage
Simple instructive example to generate a dxn matrix of random bytes:
let matrix = Rnum::newu8().ranvv(d, n).getvvu8();
First we created Rnum instance for the required type (u8). This can be saved with let statement and reused for repeated generations of values, vectors and matrices of the same end type. Next we call on it generic method ranv(d,n)
, that generates random numbers. Finally, we retrieve the matrix from its wrapper with getvvu8()
. The last two methods must agree on the target object, in this case both indicate vv
= vec of vecs
.
Polymorphic interface avoids having to use different typed functions for each primitive type. This can be too repetitive, given that there are quite a few primitive numeric types. Nevertheless, such typed functions are also available in (use ran::generators::*;
). They can be used in simple applications directly (see below, section Explicitly Typed Functions).
In lib.rs
we define three polymorphic (generic) enum types:
/// Wrapper for enum polymorphism  single value
pub enum Rnum {
F64(f64), U64(u64), I64(i64), U16(u16), U8(u8)
// Should be extended to cover all numeric types?
}
/// Wrapper for enum polymorphism  vectors
pub enum Rv {
F64(Vec<f64>), U64(Vec<u64>), I64(Vec<i64>), U16(Vec<u16>), U8(Vec<u8>)
}
/// Wrapper for enum polymorphism  vectors of vectors
pub enum Rvv {
F64(Vec<Vec<f64>>),
U64(Vec<Vec<u64>>),
I64(Vec<Vec<i64>>),
U16(Vec<Vec<i16>>),
U8(Vec<Vec<u8>>)
}
Their filling with random numbers of required types is done by their associated functions
, defined in module impls.rs
.
First create an instance of one of these types. For single random numbers, it will be enum type Rnum
, of the variant corresponding to the endtype of the random numbers wanted.
Rnum, Rv, Rvv
are just wrapper enum types, serving to communicate to the generic method(s) information about the actual type of the (random) number(s) wanted. The following example shows how to create instance variables for all them:
let rf = Rnum::newf64();
let ru = Rnum::newu64();
let ri = Rnum::newi64();
let ru16 = Rnum::newu16();
let ru8 = Rnum::newu8();
We can then apply common generic method(s) to all such variables to generate the required random numbers. For example:
println!("Random numbers in specified ranges: {}, {}, {}, {}",
rf.rannum_in(0.,100.), // wrapped f64 value 0. to 100.
ru.rannum_in(1.,1000.), // wrapped u64, 1 to 1000 (inclusive)
ri.rannum_in(10.,10.), // wrapped i64, 10 to 10 (inclusive)
ru16.rannum_in(60000.,65535.), // u16, 60000 to 65535
ru8.rannum_in(1.,6.) // wrapped u8, 1 to 6 (inclusive)
);
They all print because Display
has been implemented for these three enum types. Their inner wrapped values can be if let
pattern extracted as follows:
use anyhow::{Result,bail};
if let Rnum::F64(x) = rf { utilise the x:f64 value }
else { bail!("rf does not hold value of f64 type!") };
The else branch can be used to report disappointed type expectations, as shown (assuming here that anyhow
crate is being used for error handling). Alternatively, else
can be used to return some default value, e.g. {0_f64}
or it can be dismissed with a semicolon, using if let
as a statement, rather than as an expression. In this case, should this particular extraction attempt fail, it will be just ignored:
let uvec:Rv = ru8.ranv_in(20,1.,6.); // wrapped vec of random u8 values
if let Rv::U8(vx) = uvec {
println!("Dice roll sequence: {}", stringv(&vx)) };
This example illustrated the use of enum type Rv
, used for returning whole vector of random numbers. As can be seen, its variants are extracted in the same way as from Rnum
. (The helper function stringv
from module secondary.rs
converted the extracted vector to a String to facilitate its printing). Of course, uvec
would print as it is.
There is also enum type Rvv
for returning vectors of vectors of random numbers:
// vec of vecs using ranvv_in(d,n,min,max) and Display of Rvv
println!(
"5x5 matrix of integers in range [10,10]:\n{}",
ri.ranvv_in(5,5,10.,10.)
);
stringvv
is another utility function to enable display of generic vectors of vectors. We did not need to use it here since Dislay
is implemented for Rvv
type and we did not bother to extract the wrapped value (vector of vectors).
The results wrapped within all three return types: Rnum,Rv,Rvv
can all be pattern extracted as needed with if let
.
Alternatively, for convenience, they can all be extracted with supplied get
functions. Their names follow this convention: get+()vvv+end_type
. They just throw panic when the correct inner type is not found:
// the following line tests 'getvi64()'
let pairofints = ri.ranv(2).getvi64();
println!("2 random i64s: {}", stringv(&pairofints));
Generic Methods
Initialisation: the Self produced is Rnum
type and will contain the default value zero of the required numeric end type.
pub fn newf64() > Self
pub fn newu64() > Self
pub fn newi64() > Self
pub fn newu16() > Self
pub fn newu8() > Self
The following methods are all implemented for Rnum
, that means invoked on Rnum
type variable. Even when generating Rv
or Rvv
type results. Rnum
type input variable (self
) in all cases serves just to inform the generic method about the numeric type required for the generated values:
pub fn rannum(&self) > Self
returns a wrapped random number of one of the main types in maximum range allowed by the width of the type. The standardised range [0,1) is used for f64
.
pub fn rannum_in(&self,min:f64,max:f64) > Self
returns a wrapped random number of one of the main types in the range min,max (min,max are always f64
s for commonality). The range should not exceed the width of the type, e.g. 0.,255. for u8
. Nor should it be negative for unsigned types.
pub fn ranv(&self,d:usize) > Rv
returns a wrapped Vec of length d filled with random numbers of one of the main primitive types. Note that the whole Vec
is wrapped, not each individual element of it. Thus only one pattern extraction is needed.
pub fn ranv_in(&self,d:usize,min:f64,max:f64) > Rv
same as ranv
but using the specified range for the random values.
pub fn ranvv(&self,d:usize,n:usize) > Rvv
returns a wrapped Vec<Vec<_>>
consisting of n vectors, each of length d, filled with random numbers of one of the main primitive types. Note that only the whole result is wrapped, not each individual vector or element of it. Thus, again, only one pattern extraction is needed.
pub fn ranvv_in(&self,d:usize,n:usize,min:f64,max:f64) > Rvv
same as ranvv
but using the specified range for the random values.
There is no need to read beyond this point for normal daily use of this crate. However, there may be special circumstances, when using directly one of the typed functions is more convenient. Such as when needing only one specific end type. Another circumstance may be when wanting to use specific random number generator(s), different to the default ones used within the above methods. (Several are provided).
Explicitly Typed Functions
Utility functions to directly generate vectors of random numbers of common numeric end types:
/// Generates vector of size d,
/// filled with full range u64 random numbers.
pub fn ranvu64(d: usize) > Vec<u64>
/// Generates vector of size d, of full range i64 random numbers.
pub fn ranvi64(d: usize) > Vec<i64>
/// Generates vector of size d, of f64 random numbers in [0,1).
pub fn ranvf64(d: usize) > Vec<f64>
/// Generates vector of size d, of u16 random numbers in [0,65535].
pub fn ranvu16(d: usize) > Vec<u16>
/// Generates vector of size d, of u8 random numbers in [0,255].
pub fn ranvu8(d: usize) > Vec<u8>
Utility functions to generate vectors of vectors (matrices) of random numbers of common numeric end types:
/// Generates n vectors of size d each,
/// filled with full range u64 random numbers.
pub fn ranvvu64(d: usize, n: usize) > Vec<Vec<u64>>
/// Generates n vectors of size d each, of full range i64 random numbers.
pub fn ranvvi64(d: usize, n: usize) > Vec<Vec<i64>>
/// Generates vector of size d, of i64 random numbers
/// in the interval [min,max]. May include zero.
pub fn ranvi64_in(d: usize, min:i64, max:i64) > Vec<i64> {
/// Generates n vectors of size d each, of f64 random numbers in [0,1).
pub fn ranvvf64(d: usize, n: usize) > Vec<Vec<f64>>
/// Generates n vectors of size d each, of u8 random numbers in [0,255].
pub fn ranvvu16(d: usize, n: usize) > Vec<Vec<u16
>>
/// Generates n vectors of size d each, of u8 random numbers in [0,255].
pub fn ranvvu8(d: usize, n: usize) > Vec<Vec<u8>>
And these f64 alternatives, using the improved f64 generator xoshif64()
:
/// Generates vector of size d, of f64 random numbers in [0,1).
/// Bit slower but otherwise superior to `ranvf64`.
pub fn ranvf64_xoshi(d: usize) > Vec<f64>
/// Generates n vectors of size d each, of f64 random numbers in [0,1).
pub fn ranvvf64_xoshi(d: usize, n: usize) > Vec<Vec<f64>>
Low Level Integer Algorithms

xoshiu64()
generates u64 random numbers in full 64 bit range and 2^256 state space. That means the sequence is not going to repeat for a very long time. This algorithm is used to construct random numbers of all (unsigned) integer types and ranges up to 64 bits. 
splitmix()
also generates u64 numbers. It is used here only to generate the initial seeds for the 'xoshi' type algorithms.
Some transformation wrappers for xoshiu64()
:
/// Get random numbers of various smaller unsigned integer
/// types, by specifying the number of bits required,
/// e.g. `ran_ubits(16) as u16`
pub fn ran_ubits(bits:u8) > u64
/// Generate u64 random number in the interval [min,max].
pub fn ran_urange(min:u64, max:u64) > u64
/// Generate i64 random number in the interval [min,max].
pub fn ran_irange(min:i64, max:i64) > i64
Low Level Floating Point Algorithms

ranf64()
is a little older (George Marsaglia, 2003). It has been adapted here to generate f64 numbers in the standard range: half open interval [0,1). That means its output can be easily transformed into any other range. Its main claim to fame is its superior speed. 
xoshif64()
is also fast, though not quite as much asranf64()
but it makes up for it by quality. It has also been adapted to output f64 numbers in the standard range [0,1).
There is also a function that transforms any f64 number in standard range [0,1) to a new range:
/// Transform f64 number in [0,1) to [min,max)
pub fn ran_ftrans(rnum:f64, min:f64, max:f64) > f64
Recent Releases (Latest First)
Version 1.0.2 Fixed dimension error in ranvv_in
. Updated devdepency from devtimer
to times
. Added a timing test.
Version 1.0.1 Added generic get functions to unpack the data but note that they will only work if all the From
converters have been fully implemented. get_generic
upacks Rnum type, getv_generic
unpacks Rv type and getvv_generic
unpacks Rvv type.
Version 1.0.0 No substantive changes for a while, upgrading to Version 1.0.0.
Version 0.3.4 Improved documentation. Publicly exported set_seeds(n)
. It is now available at crate level as: use ran::{set_seeds, ....};
Version 0.3.3 Some reorganisation. Added module generators.rs
which now contains all generating code. Added get
functions to impls.rs
for easy extraction of the inner values of all supported types from Rnum, Rv, Rvv
.
Version 0.3.2 Added U16 type random numbers generation.
Version 0.3.1 Updated README.md to read more like an introductory user manual.
Version 0.3.0 Substantial revision. Completed the generic interface. Renamed some types and functions. Created two separate source modules (impls.rs, secondary.rs
) to clean up lib.rs
. Removed devdependence on indxvec.
Version 0.2.4 Extended the interface to vecs and vecs of vecs, in full range and in given range.
Version 0.2.3 Added boilerplate polymorphic interface.
Version 0.2.2 Added ran_irange, ranvi64, ranvvi64
, to generate i64 random numbers in any i64 range. Plus some appropriate tests in tests.rs
. Restricted bits argument in ran_ubits(bits:u8)
to u8, as it should never exceed even 63. Corrected some comments.
Version 0.2.0 Added tests/tests.rs
. Added general purpose xoshiu64()
which is now used to construct random numbers of all (unsigned) integer types and ranges. Reorganised, renamed and/or deleted some functions. Made the xoshi seeds also static, for ease of use. They no longer need to be explicitly passed as arguments.