rand: update README.md (#14341)

* update README for rand module

* use concrete values

* make sure code works
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Subhomoy Haldar 2022-05-09 20:29:20 +05:30 committed by GitHub
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@ -2,30 +2,59 @@
The V `rand` module provides two main ways in which users can generate pseudorandom numbers:
1. Through top-level functions in the `rand` module.
- `import rand` - Import the `rand` module.
- `rand.seed(seed_data)` to seed (optional).
- Use `rand.int()`, `rand.u32n(max)`, etc.
2. Through a generator of choice. The PRNGs are included in their respective submodules.
- `import rand.pcg32` - Import the module of the PRNG required.
- `mut rng := pcg32.PCG32RNG{}` - Initialize the struct. Note that the **`mut`** is important.
- `rng.seed(seed_data)` - optionally seed it with an array of `u32` values.
- Use `rng.int()`, `rng.u32n(max)`, etc.
## Direct Access Through The `rand` Module
```
// Import the rand module
import rand
...
// Optionally seed the default generator
rand.seed([u32(3110), 50714])
...
// Use the top-level functions
rand.u32n(100) ?
rand.int() // among others ...
```
## Through A Generator Of Choice
```
// Import the rand module
import rand
import rand.seed
// Import the module of the generator you want to use
import rand.pcg32
...
// Initialise the generator struct (note the `mut`)
mut rng := &rand.PRNG(pcg32.PCG32RNG{})
// Optionally seed the generator
rng.seed(seed.time_seed_array(pcg32.seed_len))
...
// Use functions of your choice
rng.u32n(100) ?
rng.int() // among others ...
```
## More Information
You can change the default generator to a different one. The only requirement is that
the generator must implement the `PRNG` interface. See `get_current_rng()` and `set_rng()`.
For non-uniform distributions, refer to the `rand.dist` module which defined functions for
sampling from non-uniform distributions. These functions make use of the global RNG.
**Note:** The global PRNG is not thread safe. It is recommended to use separate generators for
separate threads in multi-threaded applications. If you need to use non-uniform sampling functions,
it is recommended to generate them before use in a multi-threaded context.
separate threads in multi-threaded applications.
For sampling functions and generating random strings, see `string_from_set()` and other related
functions defined in this top-level module.
For arrays, see `rand.util`.
There are only a few extra functions that are defined only in this top-level `rand` module.
Otherwise, there is feature parity between the generator functions and the top-level functions.
# General Background
@ -38,25 +67,6 @@ This is often useful for simulations that need the same starting seed.
If you need truly random numbers that are going to be used for cryptography,
use the `crypto.rand` module.
# Guaranteed functions
The following 21 functions are guaranteed to be supported by `rand`
as well as the individual PRNGs.
- `seed(seed_data)` where `seed_data` is an array of `u32` values.
Different generators require different number of bits as the initial seed.
The smallest is 32-bits, required by `sys.SysRNG`.
Most others require 64-bits or 2 `u32` values.
- `u32()`, `u64()`, `int()`, `i64()`, `f32()`, `f64()`
- `u32n(max)`, `u64n(max)`, `intn(max)`, `i64n(max)`, `f32n(max)`, `f64n(max)`
- `u32_in_range(min, max)`, `u64_in_range(min, max)`, `int_in_range(min, max)`,
`i64_in_range(min, max)`, `f32_in_range(min, max)`, `f64_in_range(min, max)`
- `int31()`, `int63()`
There are several additional functions defined in the top-level module that rely
on the global RNG. If you want to make use of those functions with a different
PRNG, you can can change the global RNG to do so.
# Seeding Functions
All the generators are time-seeded.
@ -64,7 +74,7 @@ The helper functions publicly available in `rand.seed` module are:
1. `time_seed_array()` - returns a `[]u32` that can be directly plugged into the `seed()` functions.
2. `time_seed_32()` and `time_seed_64()` - 32-bit and 64-bit values respectively
that are generated from the current time.
that are generated from the current time.
# Caveats
@ -81,7 +91,7 @@ A workaround (if you _must_ use the libc RNG) is to:
# Notes
Please note that [math interval](https://en.wikipedia.org/wiki/Interval_(mathematics)#Including_or_excluding_endpoints) notation is used throughout
Please note that [math interval](<https://en.wikipedia.org/wiki/Interval_(mathematics)#Including_or_excluding_endpoints>) notation is used throughout
the function documentation to denote what numbers ranges include.
An example of `[0, max)` thus denotes a range with all posible values
between `0` and `max` **including** 0 but **excluding** `max`.