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rand: add full precision f32 and f64 random functions; fix f32/f64 multipliers (#16875)

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John 2023-01-19 09:21:47 -04:00 committed by GitHub
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6 changed files with 337 additions and 15 deletions

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@ -0,0 +1,59 @@
module bits
// f32_bits returns the IEEE 754 binary representation of f,
// with the sign bit of f and the result in the same bit position.
// f32_bits(f32_from_bits(x)) == x.
pub fn f32_bits(f f32) u32 {
p := u32(0)
#let buffer = new ArrayBuffer(4)
#let floatArr = new Float32Array(buffer)
#floatArr[0] = f.val
#let uintArr = new Uint32Array(buffer)
#p.val = uintArr[0]
return p
}
// f32_from_bits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// f32_from_bits(f32_bits(x)) == x.
pub fn f32_from_bits(b u32) f32 {
p := f32(0.0)
#let buffer = new ArrayBuffer(4)
#let floatArr = new Float32Array(buffer)
#let uintArr = new Uint32Array(buffer)
#uintArr[0] = Number(b.val)
#p.val = floatArr[0]
return p
}
// f64_bits returns the IEEE 754 binary representation of f,
// with the sign bit of f and the result in the same bit position,
// and f64_bits(f64_from_bits(x)) == x.
pub fn f64_bits(f f64) u64 {
p := u64(0)
#let buffer = new ArrayBuffer(8)
#let floatArr = new Float64Array(buffer)
#floatArr[0] = f.val
#let uintArr = new BigUint64Array(buffer)
#p.val = uintArr[0]
return p
}
// f64_from_bits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// f64_from_bits(f64_bits(x)) == x.
pub fn f64_from_bits(b u64) f64 {
p := 0.0
#let buffer = new ArrayBuffer(8)
#let floatArr = new Float64Array(buffer)
#let uintArr = new BigUint64Array(buffer)
#uintArr[0] = b.val
#p.val = floatArr[0]
return p
}

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@ -0,0 +1,42 @@
// Copyright (c) 2019-2022 Alexander Medvednikov. All rights reserved.
// Use of this source code is governed by an MIT license
// that can be found in the LICENSE file.
module bits
// f32_bits returns the IEEE 754 binary representation of f,
// with the sign bit of f and the result in the same bit position.
// f32_bits(f32_from_bits(x)) == x.
[inline]
pub fn f32_bits(f f32) u32 {
p := *unsafe { &u32(&f) }
return p
}
// f32_from_bits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// f32_from_bits(f32_bits(x)) == x.
[inline]
pub fn f32_from_bits(b u32) f32 {
p := *unsafe { &f32(&b) }
return p
}
// f64_bits returns the IEEE 754 binary representation of f,
// with the sign bit of f and the result in the same bit position,
// and f64_bits(f64_from_bits(x)) == x.
[inline]
pub fn f64_bits(f f64) u64 {
p := *unsafe { &u64(&f) }
return p
}
// f64_from_bits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// f64_from_bits(f64_bits(x)) == x.
[inline]
pub fn f64_from_bits(b u64) f64 {
p := *unsafe { &f64(&b) }
return p
}

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@ -11,7 +11,7 @@ import rand
... ...
// Optionally seed the default generator // Optionally seed the default generator
rand.seed([u32(3110), 50714]) rand.seed([u32(3223878742), 1732001562])
... ...
@ -61,21 +61,40 @@ Otherwise, there is feature parity between the generator functions and the top-l
A PRNG is a Pseudo Random Number Generator. A PRNG is a Pseudo Random Number Generator.
Computers cannot generate truly random numbers without an external source of noise or entropy. Computers cannot generate truly random numbers without an external source of noise or entropy.
We can use algorithms to generate sequences of seemingly random numbers, We can use algorithms to generate sequences of seemingly random numbers,
but their outputs will always be deterministic. but their outputs will always be deterministic, according to the seed values.
This is often useful for simulations that need the same starting seed.
This is often useful for simulations that need the same starting seeds.
You may be debugging a program and want to restart it with the same
seeds, or you want to verify a working program is still
operating identically after compiler or operating system updates.
If you need truly random numbers that are going to be used for cryptography, If you need truly random numbers that are going to be used for cryptography,
use the `crypto.rand` module. use the `crypto.rand` module.
# Seeding Functions # Seeding Functions
All the generators are time-seeded. All the generators are initialized with time-based seeds.
The helper functions publicly available in `rand.seed` module are: 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. 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 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.
When composing your own seeds, use "typical" u32 numbers, not small numbers. This
is especially important for PRNGs with large state, such as `mt19937`. You can create
random unsigned integers with openssl `rand` or with `v repl` as follows:
```
$ openssl rand -hex 4
e3655862
$ openssl rand -hex 4
97c4b1db
$ v repl
>>> import rand
>>> [rand.u32(),rand.u32()]
[2132382944, 2443871665]
```
# Caveats # Caveats
Note that the `sys.SysRNG` struct (in the C backend) uses `C.srand()` which sets the seed globally. Note that the `sys.SysRNG` struct (in the C backend) uses `C.srand()` which sets the seed globally.

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@ -2,11 +2,16 @@ module constants
// Commonly used constants across RNGs - some taken from "Numerical Recipes". // Commonly used constants across RNGs - some taken from "Numerical Recipes".
pub const ( pub const (
lower_mask = u64(0x00000000FFFFFFFF) lower_mask = u64(0x00000000FFFFFFFF)
max_u32 = u32(0xFFFFFFFF) max_u32 = u32(0xFFFFFFFF)
max_u64 = u64(0xFFFFFFFFFFFFFFFF) max_u64 = u64(0xFFFFFFFFFFFFFFFF)
max_u32_as_f32 = f32(max_u32) + 1 u31_mask = u32(0x7FFFFFFF)
max_u64_as_f64 = f64(max_u64) + 1 u63_mask = u64(0x7FFFFFFFFFFFFFFF)
u31_mask = u32(0x7FFFFFFF) // 23 bits for f32
u63_mask = u64(0x7FFFFFFFFFFFFFFF) ieee754_mantissa_f32_mask = (u32(1) << 23) - 1
// 52 bits for f64
ieee754_mantissa_f64_mask = (u64(1) << 52) - 1
// smallest mantissa with exponent 0 (un normalized)
reciprocal_2_23rd = 1.0 / f64(u32(1) << 23)
reciprocal_2_52nd = 1.0 / f64(u64(1) << 52)
) )

116
vlib/rand/fp_test.v Normal file
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@ -0,0 +1,116 @@
import rand
struct Histo {
mut:
lo f64
ct int
}
const (
// The sample size to be used; keep cpu time less than 5 seconds
count = 9100100
// Two sets of seeds
seeds = [[u32(2742798260), 2159764996], [u32(2135051596), 958016781]]
)
fn test_f32() {
mut histo := []Histo{}
mut candidate := f32(0.0)
histo << Histo{1.0e-9, 0}
histo << Histo{1.0e-6, 0}
histo << Histo{1.0e-4, 0}
histo << Histo{1.0e-2, 0}
histo << Histo{1.0e00, 0}
for seed in seeds {
rand.seed(seed)
for _ in 0 .. count {
candidate = rand.f32()
for mut p in histo {
if candidate < p.lo {
p.ct += 1
}
}
}
}
println(' f32 ')
println(histo)
assert histo[0].ct == 1
assert histo[1].ct == 16
assert histo[2].ct == 1802
assert histo[3].ct == 181963
assert histo[4].ct == 18200200
for mut p in histo {
p.ct = 0
}
for seed in seeds {
rand.seed(seed)
for _ in 0 .. count {
candidate = rand.f32cp()
for mut p in histo {
if candidate < p.lo {
p.ct += 1
}
}
}
}
println(' f32cp')
println(histo)
assert histo[0].ct == 0
assert histo[1].ct == 16
assert histo[2].ct == 1863
assert histo[3].ct == 142044
assert histo[4].ct == 18200200
}
fn test_f64() {
mut histo := []Histo{}
mut candidate := f64(0.0)
histo << Histo{1.0e-9, 0}
histo << Histo{1.0e-6, 0}
histo << Histo{1.0e-4, 0}
histo << Histo{1.0e-2, 0}
histo << Histo{1.0e00, 0}
for seed in seeds {
rand.seed(seed)
for _ in 0 .. count {
candidate = rand.f64()
for mut p in histo {
if candidate < p.lo {
p.ct += 1
}
}
}
}
println(' f64 ')
println(histo)
assert histo[0].ct == 0
assert histo[1].ct == 23
assert histo[2].ct == 1756
assert histo[3].ct == 182209
assert histo[4].ct == 18200200
for mut p in histo {
p.ct = 0
}
for seed in seeds {
rand.seed(seed)
for _ in 0 .. count {
candidate = rand.f64cp()
for mut p in histo {
if candidate < p.lo {
p.ct += 1
}
}
}
}
println(' f64cp')
println(histo)
assert histo[0].ct == 0
assert histo[1].ct == 17
assert histo[2].ct == 1878
assert histo[3].ct == 181754
assert histo[4].ct == 18200200
}

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@ -191,16 +191,85 @@ pub fn (mut rng PRNG) i64_in_range(min i64, max i64) !i64 {
return min + rng.i64n(max - min)! return min + rng.i64n(max - min)!
} }
// f32 returns a pseudorandom `f32` value in range `[0, 1)`. // f32 returns a pseudorandom `f32` value in range `[0, 1)`
// using rng.u32() multiplied by an f64 constant.
[inline] [inline]
pub fn (mut rng PRNG) f32() f32 { pub fn (mut rng PRNG) f32() f32 {
return f32(rng.u32()) / constants.max_u32_as_f32 return f32((rng.u32() >> 9) * constants.reciprocal_2_23rd)
} }
// f64 returns a pseudorandom `f64` value in range `[0, 1)`. // f32cp returns a pseudorandom `f32` value in range `[0, 1)`
// with full precision (mantissa random between 0 and 1
// and the exponent varies as well.)
// See https://allendowney.com/research/rand/ for background on the method.
[inline]
pub fn (mut rng PRNG) f32cp() f32 {
mut x := rng.u32()
mut exp := u32(126)
mut mask := u32(1) << 31
// check if prng returns 0; rare but keep looking for precision
if _unlikely_(x == 0) {
x = rng.u32()
exp -= 31
}
// count leading one bits and scale exponent accordingly
for {
if x & mask != 0 {
mask >>= 1
exp -= 1
} else {
break
}
}
// if we used any high-order mantissa bits; replace x
if exp < (126 - 8) {
x = rng.u32()
}
// Assumes little-endian IEEE floating point.
x = (exp << 23) | (x >> 8) & constants.ieee754_mantissa_f32_mask
return bits.f32_from_bits(x)
}
// f64 returns a pseudorandom `f64` value in range `[0, 1)`
// using rng.u64() multiplied by a constant.
[inline] [inline]
pub fn (mut rng PRNG) f64() f64 { pub fn (mut rng PRNG) f64() f64 {
return f64(rng.u64()) / constants.max_u64_as_f64 return f64((rng.u64() >> 12) * constants.reciprocal_2_52nd)
}
// f64cp returns a pseudorandom `f64` value in range `[0, 1)`
// with full precision (mantissa random between 0 and 1
// and the exponent varies as well.)
// See https://allendowney.com/research/rand/ for background on the method.
[inline]
pub fn (mut rng PRNG) f64cp() f64 {
mut x := rng.u64()
mut exp := u64(1022)
mut mask := u64(1) << 63
mut bitcount := u32(0)
// check if prng returns 0; unlikely.
if _unlikely_(x == 0) {
x = rng.u64()
exp -= 31
}
// count leading one bits and scale exponent accordingly
for {
if x & mask != 0 {
mask >>= 1
bitcount += 1
} else {
break
}
}
exp -= bitcount
if bitcount > 11 {
x = rng.u64()
}
x = (exp << 52) | (x & constants.ieee754_mantissa_f64_mask)
return bits.f64_from_bits(x)
} }
// f32n returns a pseudorandom `f32` value in range `[0, max]`. // f32n returns a pseudorandom `f32` value in range `[0, max]`.
@ -520,11 +589,23 @@ pub fn f32() f32 {
return default_rng.f32() return default_rng.f32()
} }
// f32cp returns a uniformly distributed 32-bit floating point in range `[0, 1)`
// with full precision mantissa.
pub fn f32cp() f32 {
return default_rng.f32cp()
}
// f64 returns a uniformly distributed 64-bit floating point in range `[0, 1)`. // f64 returns a uniformly distributed 64-bit floating point in range `[0, 1)`.
pub fn f64() f64 { pub fn f64() f64 {
return default_rng.f64() return default_rng.f64()
} }
// f64 returns a uniformly distributed 64-bit floating point in range `[0, 1)`
// with full precision mantissa.
pub fn f64cp() f64 {
return default_rng.f64cp()
}
// f32n returns a uniformly distributed 32-bit floating point in range `[0, max)`. // f32n returns a uniformly distributed 32-bit floating point in range `[0, max)`.
pub fn f32n(max f32) !f32 { pub fn f32n(max f32) !f32 {
return default_rng.f32n(max) return default_rng.f32n(max)