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# STC Module [crandom](../stc/crandom.h): Pseudo Random Number Generator
This describes the API of module **crandom**. It contains **stc64**, a *64-bit PRNG*. It can also generate
bounded uniform and normal distributed random numbers. See [random](https://en.cppreference.com/w/cpp/header/random)
for similar c++ functionality.
**stc64** is an extremely fast, novel PRNG by Tyge Løvset, suited for parallel usage. It features a
Weyl-sequence as part of the state. It is faster than *sfc64*, *wyhash64*, *pcg64*, and *xoshiro256\*\**
on common platforms. It does not require fast multiplication or 128-bit integer operations. It has a
256 bit state, but updates only 192 bit per generated number.
There is no *jump function*, but by incrementing the Weyl-increment by 2, it starts a new
unique 2^64 *minimum* length period. Note that for each Weyl-increment (state[3]), the period
length is about 2^126 with a high probability. For a single thread, a minimum period of 2^127
is generated when the Weyl-increment is incremented by 2 every 2^64 output.
**stc64** passes *PractRand*, tested up to 8TB output, Vigna's Hamming weight test, and simple
correlation tests, i.e. *n* interleaved streams with only one-bit differences in initial state.
For more, see the PRNG shootout by Vigna: http://prng.di.unimi.it and the debate between the authors of
xoshiro and pcg (Vigna/O'Neill) PRNGs: https://www.pcg-random.org/posts/on-vignas-pcg-critique.html
## Types
| Name | Type definition | Used to represent... |
|:-------------------|:------------------------------------------|:-----------------------------|
| `stc64_t` | `struct {uint64_t state[4];}` | The PRNG engine type |
| `stc64_uniform_t` | `struct {int64_t lower; uint64_t range;}` | Integer uniform distribution |
| `stc64_uniformf_t` | `struct {double lower, range;}` | Real number uniform distr. |
| `stc64_normalf_t` | `struct {double mean, stddev;}` | Normal distribution type |
## Header file
All cstr definitions and prototypes may be included in your C source file by including a single header file.
```c
#include "stc/crandom.h"
```
## Methods
```c
void stc64_srandom(uint64_t seed);
uint64_t stc64_random(void);
1) stc64_t stc64_init(uint64_t seed);
2) stc64_t stc64_with_seq(uint64_t seed, uint64_t seq);
3) uint64_t stc64_rand(stc64_t* rng);
4) double stc64_randf(stc64_t* rng);
5) stc64_uniform_t stc64_uniform_init(int64_t low, int64_t high);
6) int64_t stc64_uniform(stc64_t* rng, stc64_uniform_t* dist);
7) stc64_uniformf_t stc64_uniformf_init(double low, double high);
8) double stc64_uniformf(stc64_t* rng, stc64_uniformf_t* dist);
9) stc64_normalf_t stc64_normalf_init(double mean, double stddev);
10) double stc64_normalf(stc64_t* rng, stc64_normalf_t* dist);
```
`1-2)` PRNG 64-bit engine initializers. `3)` Integer generator, range \[0, 2^64).
`4)` Double RNG with range \[0, 1). `5-6)` Uniform integer RNG with range \[*low*, *high*].
`7-8)` Uniform double RNG with range \[*low*, *high*). `9-10)` Normal-distributed double
RNG, around 68% of the values fall within the range [*mean* - *stddev*, *mean* + *stddev*].
## Example
```c
#include <stdio.h>
#include <time.h>
#include <stc/crandom.h>
#include <stc/csmap.h>
#include <stc/cstr.h>
// Declare int -> int sorted map. Uses typetag 'i' for ints.
using_csmap(i, int, size_t);
int main()
{
enum {N = 10000000};
const double Mean = -12.0, StdDev = 6.0, Scale = 74;
printf("Demo of gaussian / normal distribution of %d random samples\n", N);
// Setup random engine with normal distribution.
uint64_t seed = time(NULL);
stc64_t rng = stc64_init(seed);
stc64_normalf_t dist = stc64_normalf_init(Mean, StdDev);
// Create histogram map
csmap_i mhist = csmap_i_init();
c_forrange (N) {
int index = (int) round( stc64_normalf(&rng, &dist) );
++ csmap_i_emplace(&mhist, index, 0).first->second;
}
// Print the gaussian bar chart
cstr_t bar = cstr_init();
c_foreach (i, csmap_i, mhist) {
size_t n = (size_t) (i.ref->second * StdDev * Scale * 2.5 / N);
if (n > 0) {
cstr_resize(&bar, n, '*');
printf("%4d %s\n", i.ref->first, bar.str);
}
}
// Cleanup
cstr_del(&bar);
csmap_i_del(&mhist);
}
```
Output:
```
Demo of gaussian / normal distribution of 10000000 random samples
-29 *
-28 **
-27 ***
-26 ****
-25 *******
-24 *********
-23 *************
-22 ******************
-21 ***********************
-20 ******************************
-19 *************************************
-18 ********************************************
-17 ****************************************************
-16 ***********************************************************
-15 *****************************************************************
-14 *********************************************************************
-13 ************************************************************************
-12 *************************************************************************
-11 ************************************************************************
-10 *********************************************************************
-9 *****************************************************************
-8 ***********************************************************
-7 ****************************************************
-6 ********************************************
-5 *************************************
-4 ******************************
-3 ***********************
-2 ******************
-1 *************
0 *********
1 *******
2 ****
3 ***
4 **
5 *
```
|
