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Float sampling: improve high precision sampling; add mean test
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(The mean test is totally inadequate for checking high precision.)
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@dhardy @sicking
dhardy authored and sicking committed Jul 1, 2018
1 parent 8fe80f6 commit 7ec9a59
Showing 1 changed file with 76 additions and 49 deletions.
125 changes: 76 additions & 49 deletions src/distributions/float.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@

//! Basic floating-point number distributions

use core::mem;
use core::{cmp, mem};
use Rng;
use distributions::{Distribution, Standard};
use distributions::utils::CastFromInt;
Expand Down Expand Up @@ -98,22 +98,20 @@ impl<F: HPFloatHelper> HighPrecision<F> {
}

/// Generate a floating point number in the half-open interval `[0, 1)` with a
/// uniform distribution.
/// uniform distribution, with as much precision as the floating-point type
/// can represent, including sub-normals.
///
/// This is different from `Uniform` in that it uses all 32 bits of an RNG for a
/// `f32`, instead of only 23, the number of bits that fit in a floats fraction
/// (or 64 instead of 52 bits for a `f64`).
/// Technically 0 is representable, but the probability of occurrence is
/// remote (1 in 2^149 for `f32` or 1 in 2^1074 for `f64`).
///
/// The smallest interval between values that can be generated is 2^-32
/// (2.3283064e-10) for `f32`, and 2^-64 (5.421010862427522e-20) for `f64`.
/// But this interval increases further away from zero because of limitations of
/// the floating point format. Close to 1.0 the interval is 2^-24 (5.9604645e-8)
/// for `f32`, and 2^-53 (1.1102230246251565) for `f64`. Compare this with
/// `Uniform`, which has a fixed interval of 2^23 and 2^-52 respectively.
///
/// Note: in the future this may change change to request even more bits from
/// the RNG if the value gets very close to 0.0, so it always has as many digits
/// of precision as the float can represent.
/// This is different from `Uniform` in that it uses as many random bits as
/// required to get high precision close to 0. Normally only a single call to
/// the source RNG is required (32 bits for `f32` or 64 bits for `f64`); 1 in
/// 2^9 (`f32`) or 2^12 (`f64`) samples need an extra call; of these 1 in 2^32
/// or 1 in 2^64 require a third call, etc.; i.e. even for `f32` a third call is
/// almost impossible to observe with an unbiased RNG. Due to the extra logic
/// there is some performance overhead relative to `Uniform`; this is more
/// significant for `f32` than for `f64`.
///
/// # Example
/// ```rust
Expand Down Expand Up @@ -229,24 +227,10 @@ float_impls! { f64x8, u64x8, f64, u64, 52, 1023 }


macro_rules! high_precision_float_impls {
($ty:ty, $uty:ty, $ity:ty, $fraction_bits:expr, $exponent_bits:expr) => {
($ty:ty, $uty:ty, $ity:ty, $fraction_bits:expr, $exponent_bits:expr, $exponent_bias:expr) => {
impl Distribution<$ty> for HighPrecision01 {
/// Generate a floating point number in the half-open interval
/// `[0, 1)` with a uniform distribution.
///
/// This is different from `Uniform` in that it uses all 32 bits
/// of an RNG for a `f32`, instead of only 23, the number of bits
/// that fit in a floats fraction (or 64 instead of 52 bits for a
/// `f64`).
///
/// # Example
/// ```rust
/// use rand::{NewRng, SmallRng, Rng};
/// use rand::distributions::HighPrecision01;
///
/// let val: f32 = SmallRng::new().sample(HighPrecision01);
/// println!("f32 from [0,1): {}", val);
/// ```
/// `[0, 1)` with a uniform distribution. See [`HighPrecision01`].
///
/// # Algorithm
/// (Note: this description used values that apply to `f32` to
Expand All @@ -255,34 +239,50 @@ macro_rules! high_precision_float_impls {
/// The trick to generate a uniform distribution over [0,1) is to
/// set the exponent to the -log2 of the remaining random bits. A
/// simpler alternative to -log2 is to count the number of trailing
/// zero's of the random bits.
/// zeros in the random bits. In the case where all bits are zero,
/// we simply generate a new random number and add the number of
/// trailing zeros to the previous count (up to maximum exponent).
///
/// Each exponent is responsible for a piece of the distribution
/// between [0,1). The exponent -1 fills the part [0.5,1). -2 fills
/// [0.25,0.5). The lowest exponent we can get is -10. So a problem
/// with this method is that we can not fill the part between zero
/// and the part from -10. The solution is to treat numbers with an
/// exponent of -10 as if they have -9 as exponent, and substract
/// 2^-9 (implemented in the `fallback` function).
/// between [0,1). We take the above exponent, add 1 and negate;
/// thus with probability 1/2 we have exponent -1 which fills the
/// range [0.5,1); with probability 1/4 we have exponent -2 which
/// fills the range [0.25,0.5), etc. If the exponent reaches the
/// minimum allowed, the floating-point format drops the implied
/// fraction bit, thus allowing numbers down to 0 to be sampled.
///
/// [`HighPrecision01`]: struct.HighPrecision01.html
#[inline]
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
// Unusual case. Separate function to allow inlining of rest.
#[inline(never)]
fn fallback(fraction: $uty) -> $ty {
let float_size = (mem::size_of::<$ty>() * 8) as i32;
let min_exponent = $fraction_bits as i32 - float_size;
let adjust = // 2^MIN_EXPONENT
(0 as $uty).into_float_with_exponent(min_exponent);
fraction.into_float_with_exponent(min_exponent) - adjust
fn fallback<R: Rng + ?Sized>(mut exp: i32, fraction: $uty, rng: &mut R) -> $ty {
// Performance impact of code here is negligible.
let bits = rng.gen::<$uty>();
exp += bits.trailing_zeros() as i32;
// If RNG were guaranteed unbiased we could skip the
// check against exp; unfortunately it may be.
// Worst case ("zeros" RNG) has recursion depth 16.
if bits == 0 && exp < $exponent_bias {
return fallback(exp, fraction, rng);
}
exp = cmp::min(exp, $exponent_bias);
fraction.into_float_with_exponent(-exp)
}

let fraction_mask = (1 << $fraction_bits) - 1;
let value: $uty = rng.gen();

let fraction = value & fraction_mask;
let remaining = value >> $fraction_bits;
// If `remaing ==0` we end up in the lowest exponent, which
// needs special treatment.
if remaining == 0 { return fallback(fraction) }
if remaining == 0 {
// exp is compile-time constant so this reduces to a function call:
let size_bits = (mem::size_of::<$ty>() * 8) as i32;
let exp = (size_bits - $fraction_bits as i32) + 1;
return fallback(exp, fraction, rng);
}

// Usual case: exponent from -1 to -9 (f32) or -12 (f64)
let exp = remaining.trailing_zeros() as i32 + 1;
fraction.into_float_with_exponent(-exp)
}
Expand Down Expand Up @@ -444,8 +444,8 @@ macro_rules! high_precision_float_impls {
}
}

high_precision_float_impls! { f32, u32, i32, 23, 8 }
high_precision_float_impls! { f64, u64, i64, 52, 11 }
high_precision_float_impls! { f32, u32, i32, 23, 8, 127 }
high_precision_float_impls! { f64, u64, i64, 52, 11, 1023 }


#[cfg(test)]
Expand Down Expand Up @@ -729,4 +729,31 @@ mod tests {
assert_eq!(ones.sample::<f32, _>(HighPrecision01), 0.99999994);
assert_eq!(ones.sample::<f64, _>(HighPrecision01), 0.9999999999999999);
}

#[cfg(feature="std")] mod mean {
use Rng;
use distributions::{Standard, HighPrecision01};

macro_rules! test_mean {
($name:ident, $ty:ty, $distr:expr) => {
#[test]
fn $name() {
// TODO: no need to &mut here:
let mut r = ::test::rng(602);
let mut total: $ty = 0.0;
const N: u32 = 1_000_000;
for _ in 0..N {
total += r.sample::<$ty, _>($distr);
}
let avg = total / (N as $ty);
//println!("average over {} samples: {}", N, avg);
assert!(0.499 < avg && avg < 0.501);
}
} }

test_mean!(test_mean_f32, f32, Standard);
test_mean!(test_mean_f64, f64, Standard);
test_mean!(test_mean_high_f32, f32, HighPrecision01);
test_mean!(test_mean_high_f64, f64, HighPrecision01);
}
}

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