Merge remote-tracking branch 'pitdicker/port_range'

Author: dhardy

benches/distributions.rs

@@ -38,8 +38,8 @@ distr!(distr_range_i64, i64, Range::new(3i64, 12345678901234));
 #[cfg(feature = "i128_support")]
 distr!(distr_range_i128, i128, Range::new(-12345678901234i128, 12345678901234567890));
 
-distr!(distr_range_float32, f32, Range::new(2.26f32, 2.319));
-distr!(distr_range_float, f64, Range::new(2.26f64, 2.319));
+distr!(distr_range_f32, f32, Range::new(2.26f32, 2.319));
+distr!(distr_range_f64, f64, Range::new(2.26f64, 2.319));
 
 // uniform
 distr!(distr_uniform_i8, i8, Uniform);
@@ -53,13 +53,8 @@ distr!(distr_uniform_bool, bool, Uniform);
 distr!(distr_uniform_alphanumeric, char, Alphanumeric);
 distr!(distr_uniform_codepoint, char, Uniform);
 
-distr!(distr_uniform01_float32, f32, Uniform);
-distr!(distr_closed01_float32, f32, Closed01);
-distr!(distr_open01_float32, f32, Open01);
-
-distr!(distr_uniform01_float, f64, Uniform);
-distr!(distr_closed01_float, f64, Closed01);
-distr!(distr_open01_float, f64, Open01);
+distr!(distr_uniform_f32, f32, Uniform);
+distr!(distr_uniform_f64, f64, Uniform);
 
 // distributions
 distr!(distr_exp, f64, Exp::new(2.71828 * 3.14159));

src/distributions/float.rs

@@ -14,203 +14,86 @@ use core::mem;
 use Rng;
 use distributions::{Distribution, Uniform};
 
-
-/// A distribution to sample floating point numbers uniformly in the open
-/// interval `(0, 1)` (not including either endpoint).
-///
-/// See also: [`Closed01`] for the closed `[0, 1]`; [`Uniform`] for the
-/// half-open `[0, 1)`.
-///
-/// # Example
-/// ```rust
-/// use rand::{weak_rng, Rng};
-/// use rand::distributions::Open01;
-///
-/// let val: f32 = weak_rng().sample(Open01);
-/// println!("f32 from (0,1): {}", val);
-/// ```
-///
-/// [`Uniform`]: struct.Uniform.html
-/// [`Closed01`]: struct.Closed01.html
-#[derive(Clone, Copy, Debug)]
-pub struct Open01;
-
-/// A distribution to sample floating point numbers uniformly in the closed
-/// interval `[0, 1]` (including both endpoints).
-///
-/// See also: [`Open01`] for the open `(0, 1)`; [`Uniform`] for the half-open
-/// `[0, 1)`.
-///
-/// # Example
-/// ```rust
-/// use rand::{weak_rng, Rng};
-/// use rand::distributions::Closed01;
-///
-/// let val: f32 = weak_rng().sample(Closed01);
-/// println!("f32 from [0,1]: {}", val);
-/// ```
-///
-/// [`Uniform`]: struct.Uniform.html
-/// [`Open01`]: struct.Open01.html
-#[derive(Clone, Copy, Debug)]
-pub struct Closed01;
-
-
-// Return the next random f32 selected from the half-open
-// interval `[0, 1)`.
-//
-// This uses a technique described by Saito and Matsumoto at
-// MCQMC'08. Given that the IEEE floating point numbers are
-// uniformly distributed over [1,2), we generate a number in
-// this range and then offset it onto the range [0,1). Our
-// choice of bits (masking v. shifting) is arbitrary and
-// should be immaterial for high quality generators. For low
-// quality generators (ex. LCG), prefer bitshifting due to
-// correlation between sequential low order bits.
-//
-// See:
-// A PRNG specialized in double precision floating point numbers using
-// an affine transition
-//
-// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/dSFMT.pdf>
-// * <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/dSFMT-slide-e.pdf>
-impl Distribution<f32> for Uniform {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f32 {
-        const UPPER_MASK: u32 = 0x3F800000;
-        const LOWER_MASK: u32 = 0x7FFFFF;
-        let tmp = UPPER_MASK | (rng.next_u32() & LOWER_MASK);
-        let result: f32 = unsafe { mem::transmute(tmp) };
-        result - 1.0
-    }
-}
-impl Distribution<f64> for Uniform {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        const UPPER_MASK: u64 = 0x3FF0000000000000;
-        const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF;
-        let tmp = UPPER_MASK | (rng.next_u64() & LOWER_MASK);
-        let result: f64 = unsafe { mem::transmute(tmp) };
-        result - 1.0
-    }
+pub(crate) trait IntoFloat {
+    type F;
+
+    /// Helper method to combine the fraction and a contant exponent into a
+    /// float.
+    ///
+    /// Only the least significant bits of `self` may be set, 23 for `f32` and
+    /// 52 for `f64`.
+    /// The resulting value will fall in a range that depends on the exponent.
+    /// As an example the range with exponent 0 will be
+    /// [2<sup>0</sup>..2<sup>1</sup>), which is [1..2).
+    #[inline(always)]
+    fn into_float_with_exponent(self, exponent: i32) -> Self::F;
 }
 
 macro_rules! float_impls {
-    ($mod_name:ident, $ty:ty, $mantissa_bits:expr) => {
-        mod $mod_name {
-            use Rng;
-            use distributions::{Distribution};
-            use super::{Open01, Closed01};
-
-            const SCALE: $ty = (1u64 << $mantissa_bits) as $ty;
-
-            impl Distribution<$ty> for Open01 {
-                #[inline]
-                fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                    // add 0.5 * epsilon, so that smallest number is
-                    // greater than 0, and largest number is still
-                    // less than 1, specifically 1 - 0.5 * epsilon.
-                    let x: $ty = rng.gen();
-                    x + 0.5 / SCALE
-                }
+    ($ty:ty, $uty:ty, $fraction_bits:expr, $exponent_bias:expr,
+     $next_u:ident) => {
+        impl IntoFloat for $uty {
+            type F = $ty;
+            #[inline(always)]
+            fn into_float_with_exponent(self, exponent: i32) -> $ty {
+                // The exponent is encoded using an offset-binary representation
+                let exponent_bits =
+                    (($exponent_bias + exponent) as $uty) << $fraction_bits;
+                unsafe { mem::transmute(self | exponent_bits) }
             }
-            impl Distribution<$ty> for Closed01 {
-                #[inline]
-                fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                    // rescale so that 1.0 - epsilon becomes 1.0
-                    // precisely.
-                    let x: $ty = rng.gen();
-                    x * SCALE / (SCALE - 1.0)
-                }
+        }
+
+        impl Distribution<$ty> for Uniform {
+            /// Generate a floating point number in the open interval `(0, 1)`
+            /// (not including either endpoint) with a uniform distribution.
+            ///
+            /// # Example
+            /// ```rust
+            /// use rand::{weak_rng, Rng};
+            /// use rand::distributions::Uniform;
+            ///
+            /// let val: f32 = weak_rng().sample(Uniform);
+            /// println!("f32 from (0,1): {}", val);
+            /// ```
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                const EPSILON: $ty = 1.0 / (1u64 << $fraction_bits) as $ty;
+                let float_size = mem::size_of::<$ty>() * 8;
+
+                let value = rng.$next_u();
+                let fraction = value >> (float_size - $fraction_bits);
+                fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
             }
         }
     }
 }
-float_impls! { f64_rand_impls, f64, 52 }
-float_impls! { f32_rand_impls, f32, 23 }
+float_impls! { f32, u32, 23, 127, next_u32 }
+float_impls! { f64, u64, 52, 1023, next_u64 }
 
 
 #[cfg(test)]
 mod tests {
     use Rng;
     use mock::StepRng;
-    use distributions::{Open01, Closed01};
 
     const EPSILON32: f32 = ::core::f32::EPSILON;
     const EPSILON64: f64 = ::core::f64::EPSILON;
 
     #[test]
     fn floating_point_edge_cases() {
         let mut zeros = StepRng::new(0, 0);
-        assert_eq!(zeros.gen::<f32>(), 0.0);
-        assert_eq!(zeros.gen::<f64>(), 0.0);
-        
-        let mut one = StepRng::new(1, 0);
-        assert_eq!(one.gen::<f32>(), EPSILON32);
-        assert_eq!(one.gen::<f64>(), EPSILON64);
-        
-        let mut max = StepRng::new(!0, 0);
-        assert_eq!(max.gen::<f32>(), 1.0 - EPSILON32);
-        assert_eq!(max.gen::<f64>(), 1.0 - EPSILON64);
-    }
+        assert_eq!(zeros.gen::<f32>(), 0.0 + EPSILON32 / 2.0);
+        assert_eq!(zeros.gen::<f64>(), 0.0 + EPSILON64 / 2.0);
 
-    #[test]
-    fn fp_closed_edge_cases() {
-        let mut zeros = StepRng::new(0, 0);
-        assert_eq!(zeros.sample::<f32, _>(Closed01), 0.0);
-        assert_eq!(zeros.sample::<f64, _>(Closed01), 0.0);
-        
-        let mut one = StepRng::new(1, 0);
-        let one32 = one.sample::<f32, _>(Closed01);
-        let one64 = one.sample::<f64, _>(Closed01);
-        assert!(EPSILON32 < one32 && one32 < EPSILON32 * 1.01);
-        assert!(EPSILON64 < one64 && one64 < EPSILON64 * 1.01);
-        
-        let mut max = StepRng::new(!0, 0);
-        assert_eq!(max.sample::<f32, _>(Closed01), 1.0);
-        assert_eq!(max.sample::<f64, _>(Closed01), 1.0);
-    }
-
-    #[test]
-    fn fp_open_edge_cases() {
-        let mut zeros = StepRng::new(0, 0);
-        assert_eq!(zeros.sample::<f32, _>(Open01), 0.0 + EPSILON32 / 2.0);
-        assert_eq!(zeros.sample::<f64, _>(Open01), 0.0 + EPSILON64 / 2.0);
-        
-        let mut one = StepRng::new(1, 0);
-        let one32 = one.sample::<f32, _>(Open01);
-        let one64 = one.sample::<f64, _>(Open01);
+        let mut one = StepRng::new(1 << 9, 0);
+        let one32 = one.gen::<f32>();
         assert!(EPSILON32 < one32 && one32 < EPSILON32 * 2.0);
-        assert!(EPSILON64 < one64 && one64 < EPSILON64 * 2.0);
-        
-        let mut max = StepRng::new(!0, 0);
-        assert_eq!(max.sample::<f32, _>(Open01), 1.0 - EPSILON32 / 2.0);
-        assert_eq!(max.sample::<f64, _>(Open01), 1.0 - EPSILON64 / 2.0);
-    }
 
-    #[test]
-    fn rand_open() {
-        // this is unlikely to catch an incorrect implementation that
-        // generates exactly 0 or 1, but it keeps it sane.
-        let mut rng = ::test::rng(510);
-        for _ in 0..1_000 {
-            // strict inequalities
-            let f: f64 = rng.sample(Open01);
-            assert!(0.0 < f && f < 1.0);
-
-            let f: f32 = rng.sample(Open01);
-            assert!(0.0 < f && f < 1.0);
-        }
-    }
-
-    #[test]
-    fn rand_closed() {
-        let mut rng = ::test::rng(511);
-        for _ in 0..1_000 {
-            // strict inequalities
-            let f: f64 = rng.sample(Closed01);
-            assert!(0.0 <= f && f <= 1.0);
+        let mut one = StepRng::new(1 << 12, 0);
+        let one64 = one.gen::<f64>();
+        assert!(EPSILON64 < one64 && one64 < EPSILON64 * 2.0);
 
-            let f: f32 = rng.sample(Closed01);
-            assert!(0.0 <= f && f <= 1.0);
-        }
+        let mut max = StepRng::new(!0, 0);
+        assert_eq!(max.gen::<f32>(), 1.0 - EPSILON32 / 2.0);
+        assert_eq!(max.gen::<f64>(), 1.0 - EPSILON64 / 2.0);
     }
 }

src/distributions/gamma.rs

@@ -17,7 +17,7 @@ use self::ChiSquaredRepr::*;
 
 use {Rng};
 use distributions::normal::StandardNormal;
-use distributions::{Distribution, Exp, Open01};
+use distributions::{Distribution, Exp, Uniform};
 
 /// The Gamma distribution `Gamma(shape, scale)` distribution.
 ///
@@ -144,7 +144,7 @@ impl Distribution<f64> for Gamma {
 }
 impl Distribution<f64> for GammaSmallShape {
     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let u: f64 = rng.sample(Open01);
+        let u: f64 = rng.sample(Uniform);
 
         self.large_shape.sample(rng) * u.powf(self.inv_shape)
     }
@@ -159,7 +159,7 @@ impl Distribution<f64> for GammaLargeShape {
             }
 
             let v = v_cbrt * v_cbrt * v_cbrt;
-            let u: f64 = rng.sample(Open01);
+            let u: f64 = rng.sample(Uniform);
 
             let x_sqr = x * x;
             if u < 1.0 - 0.0331 * x_sqr * x_sqr ||