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88 lines
3.9 KiB
Markdown
88 lines
3.9 KiB
Markdown
# Description
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The V `rand` module provides two main ways in which users can generate pseudorandom numbers:
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1. Through top-level functions in the `rand` module.
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- `import rand` - Import the `rand` module.
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- `rand.seed(seed_data)` to seed (optional).
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- Use `rand.int()`, `rand.u32n(max)`, etc.
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2. Through a generator of choice. The PRNGs are included in their respective submodules.
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- `import rand.pcg32` - Import the module of the PRNG required.
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- `mut rng := pcg32.PCG32RNG{}` - Initialize the struct. Note that the **`mut`** is important.
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- `rng.seed(seed_data)` - optionally seed it with an array of `u32` values.
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- Use `rng.int()`, `rng.u32n(max)`, etc.
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You can change the default generator to a different one. The only requirement is that
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the generator must implement the `PRNG` interface. See `get_current_rng()` and `set_rng()`.
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For non-uniform distributions, refer to the `rand.dist` module which defined functions for
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sampling from non-uniform distributions. These functions make use of the global RNG.
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**Note:** The global PRNG is not thread safe. It is recommended to use separate generators for
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separate threads in multi-threaded applications. If you need to use non-uniform sampling functions,
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it is recommended to generate them before use in a multi-threaded context.
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For sampling functions and generating random strings, see `string_from_set()` and other related
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functions defined in this top-level module.
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For arrays, see `rand.util`.
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# General Background
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A PRNG is a Pseudo Random Number Generator.
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Computers cannot generate truly random numbers without an external source of noise or entropy.
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We can use algorithms to generate sequences of seemingly random numbers,
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but their outputs will always be deterministic.
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This is often useful for simulations that need the same starting seed.
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If you need truly random numbers that are going to be used for cryptography,
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use the `crypto.rand` module.
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# Guaranteed functions
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The following 21 functions are guaranteed to be supported by `rand`
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as well as the individual PRNGs.
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- `seed(seed_data)` where `seed_data` is an array of `u32` values.
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Different generators require different number of bits as the initial seed.
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The smallest is 32-bits, required by `sys.SysRNG`.
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Most others require 64-bits or 2 `u32` values.
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- `u32()`, `u64()`, `int()`, `i64()`, `f32()`, `f64()`
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- `u32n(max)`, `u64n(max)`, `intn(max)`, `i64n(max)`, `f32n(max)`, `f64n(max)`
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- `u32_in_range(min, max)`, `u64_in_range(min, max)`, `int_in_range(min, max)`,
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`i64_in_range(min, max)`, `f32_in_range(min, max)`, `f64_in_range(min, max)`
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- `int31()`, `int63()`
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There are several additional functions defined in the top-level module that rely
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on the global RNG. If you want to make use of those functions with a different
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PRNG, you can can change the global RNG to do so.
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# Seeding Functions
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All the generators are time-seeded.
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The helper functions publicly available in `rand.seed` module are:
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1. `time_seed_array()` - returns a `[]u32` that can be directly plugged into the `seed()` functions.
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2. `time_seed_32()` and `time_seed_64()` - 32-bit and 64-bit values respectively
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that are generated from the current time.
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# Caveats
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Note that the `sys.SysRNG` struct (in the C backend) uses `C.srand()` which sets the seed globally.
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Consequently, all instances of the RNG will be affected.
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This problem does not arise for the other RNGs.
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A workaround (if you _must_ use the libc RNG) is to:
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1. Seed the first instance.
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2. Generate all values required.
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3. Seed the second instance.
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4. Generate all values required.
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5. And so on...
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# Notes
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Please note that [math interval](https://en.wikipedia.org/wiki/Interval_(mathematics)#Including_or_excluding_endpoints) notation is used throughout
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the function documentation to denote what numbers ranges include.
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An example of `[0, max)` thus denotes a range with all posible values
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between `0` and `max` **including** 0 but **excluding** `max`.
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