logo
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Basic floating-point number distributions

use crate::distributions::utils::FloatSIMDUtils;
use crate::distributions::{Distribution, Standard};
use crate::Rng;
use core::mem;
#[cfg(feature = "simd_support")] use packed_simd::*;

/// A distribution to sample floating point numbers uniformly in the half-open
/// interval `(0, 1]`, i.e. including 1 but not 0.
///
/// All values that can be generated are of the form `n * ε/2`. For `f32`
/// the 24 most significant random bits of a `u32` are used and for `f64` the
/// 53 most significant bits of a `u64` are used. The conversion uses the
/// multiplicative method.
///
/// See also: [`Standard`] which samples from `[0, 1)`, [`Open01`]
/// which samples from `(0, 1)` and [`Uniform`] which samples from arbitrary
/// ranges.
///
/// # Example
/// ```
/// use rand::{thread_rng, Rng};
/// use rand::distributions::OpenClosed01;
///
/// let val: f32 = thread_rng().sample(OpenClosed01);
/// println!("f32 from (0, 1): {}", val);
/// ```
///
/// [`Standard`]: crate::distributions::Standard
/// [`Open01`]: crate::distributions::Open01
/// [`Uniform`]: crate::distributions::uniform::Uniform
#[derive(Clone, Copy, Debug)]
pub struct OpenClosed01;

/// A distribution to sample floating point numbers uniformly in the open
/// interval `(0, 1)`, i.e. not including either endpoint.
///
/// All values that can be generated are of the form `n * ε + ε/2`. For `f32`
/// the 23 most significant random bits of an `u32` are used, for `f64` 52 from
/// an `u64`. The conversion uses a transmute-based method.
///
/// See also: [`Standard`] which samples from `[0, 1)`, [`OpenClosed01`]
/// which samples from `(0, 1]` and [`Uniform`] which samples from arbitrary
/// ranges.
///
/// # Example
/// ```
/// use rand::{thread_rng, Rng};
/// use rand::distributions::Open01;
///
/// let val: f32 = thread_rng().sample(Open01);
/// println!("f32 from (0, 1): {}", val);
/// ```
///
/// [`Standard`]: crate::distributions::Standard
/// [`OpenClosed01`]: crate::distributions::OpenClosed01
/// [`Uniform`]: crate::distributions::uniform::Uniform
#[derive(Clone, Copy, Debug)]
pub struct Open01;


// This trait is needed by both this lib and rand_distr hence is a hidden export
#[doc(hidden)]
pub 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).
    fn into_float_with_exponent(self, exponent: i32) -> Self::F;
}

macro_rules! float_impls {
    ($ty:ident, $uty:ident, $f_scalar:ident, $u_scalar:ty,
     $fraction_bits:expr, $exponent_bias:expr) => {
        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: $u_scalar =
                    (($exponent_bias + exponent) as $u_scalar) << $fraction_bits;
                $ty::from_bits(self | exponent_bits)
            }
        }

        impl Distribution<$ty> for Standard {
            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
                // Multiply-based method; 24/53 random bits; [0, 1) interval.
                // We use the most significant bits because for simple RNGs
                // those are usually more random.
                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
                let precision = $fraction_bits + 1;
                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);

                let value: $uty = rng.gen();
                let value = value >> (float_size - precision);
                scale * $ty::cast_from_int(value)
            }
        }

        impl Distribution<$ty> for OpenClosed01 {
            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
                // Multiply-based method; 24/53 random bits; (0, 1] interval.
                // We use the most significant bits because for simple RNGs
                // those are usually more random.
                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
                let precision = $fraction_bits + 1;
                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);

                let value: $uty = rng.gen();
                let value = value >> (float_size - precision);
                // Add 1 to shift up; will not overflow because of right-shift:
                scale * $ty::cast_from_int(value + 1)
            }
        }

        impl Distribution<$ty> for Open01 {
            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
                // Transmute-based method; 23/52 random bits; (0, 1) interval.
                // We use the most significant bits because for simple RNGs
                // those are usually more random.
                use core::$f_scalar::EPSILON;
                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;

                let value: $uty = rng.gen();
                let fraction = value >> (float_size - $fraction_bits);
                fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
            }
        }
    }
}

float_impls! { f32, u32, f32, u32, 23, 127 }
float_impls! { f64, u64, f64, u64, 52, 1023 }

#[cfg(feature = "simd_support")]
float_impls! { f32x2, u32x2, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x4, u32x4, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x8, u32x8, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x16, u32x16, f32, u32, 23, 127 }

#[cfg(feature = "simd_support")]
float_impls! { f64x2, u64x2, f64, u64, 52, 1023 }
#[cfg(feature = "simd_support")]
float_impls! { f64x4, u64x4, f64, u64, 52, 1023 }
#[cfg(feature = "simd_support")]
float_impls! { f64x8, u64x8, f64, u64, 52, 1023 }


#[cfg(test)]
mod tests {
    use super::*;
    use crate::rngs::mock::StepRng;

    const EPSILON32: f32 = ::core::f32::EPSILON;
    const EPSILON64: f64 = ::core::f64::EPSILON;

    macro_rules! test_f32 {
        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
            #[test]
            fn $fnn() {
                // Standard
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.gen::<$ty>(), $ZERO);
                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);

                // OpenClosed01
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);

                // Open01
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
                let mut one = StepRng::new(1 << 9 | 1 << (9 + 32), 0);
                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
            }
        };
    }
    test_f32! { f32_edge_cases, f32, 0.0, EPSILON32 }
    #[cfg(feature = "simd_support")]
    test_f32! { f32x2_edge_cases, f32x2, f32x2::splat(0.0), f32x2::splat(EPSILON32) }
    #[cfg(feature = "simd_support")]
    test_f32! { f32x4_edge_cases, f32x4, f32x4::splat(0.0), f32x4::splat(EPSILON32) }
    #[cfg(feature = "simd_support")]
    test_f32! { f32x8_edge_cases, f32x8, f32x8::splat(0.0), f32x8::splat(EPSILON32) }
    #[cfg(feature = "simd_support")]
    test_f32! { f32x16_edge_cases, f32x16, f32x16::splat(0.0), f32x16::splat(EPSILON32) }

    macro_rules! test_f64 {
        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
            #[test]
            fn $fnn() {
                // Standard
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.gen::<$ty>(), $ZERO);
                let mut one = StepRng::new(1 << 11, 0);
                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);

                // OpenClosed01
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
                let mut one = StepRng::new(1 << 11, 0);
                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);

                // Open01
                let mut zeros = StepRng::new(0, 0);
                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
                let mut one = StepRng::new(1 << 12, 0);
                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
                let mut max = StepRng::new(!0, 0);
                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
            }
        };
    }
    test_f64! { f64_edge_cases, f64, 0.0, EPSILON64 }
    #[cfg(feature = "simd_support")]
    test_f64! { f64x2_edge_cases, f64x2, f64x2::splat(0.0), f64x2::splat(EPSILON64) }
    #[cfg(feature = "simd_support")]
    test_f64! { f64x4_edge_cases, f64x4, f64x4::splat(0.0), f64x4::splat(EPSILON64) }
    #[cfg(feature = "simd_support")]
    test_f64! { f64x8_edge_cases, f64x8, f64x8::splat(0.0), f64x8::splat(EPSILON64) }

    #[test]
    fn value_stability() {
        fn test_samples<T: Copy + core::fmt::Debug + PartialEq, D: Distribution<T>>(
            distr: &D, zero: T, expected: &[T],
        ) {
            let mut rng = crate::test::rng(0x6f44f5646c2a7334);
            let mut buf = [zero; 3];
            for x in &mut buf {
                *x = rng.sample(&distr);
            }
            assert_eq!(&buf, expected);
        }

        test_samples(&Standard, 0f32, &[0.0035963655, 0.7346052, 0.09778172]);
        test_samples(&Standard, 0f64, &[
            0.7346051961657583,
            0.20298547462974248,
            0.8166436635290655,
        ]);

        test_samples(&OpenClosed01, 0f32, &[0.003596425, 0.73460525, 0.09778178]);
        test_samples(&OpenClosed01, 0f64, &[
            0.7346051961657584,
            0.2029854746297426,
            0.8166436635290656,
        ]);

        test_samples(&Open01, 0f32, &[0.0035963655, 0.73460525, 0.09778172]);
        test_samples(&Open01, 0f64, &[
            0.7346051961657584,
            0.20298547462974248,
            0.8166436635290656,
        ]);

        #[cfg(feature = "simd_support")]
        {
            // We only test a sub-set of types here. Values are identical to
            // non-SIMD types; we assume this pattern continues across all
            // SIMD types.

            test_samples(&Standard, f32x2::new(0.0, 0.0), &[
                f32x2::new(0.0035963655, 0.7346052),
                f32x2::new(0.09778172, 0.20298547),
                f32x2::new(0.34296435, 0.81664366),
            ]);

            test_samples(&Standard, f64x2::new(0.0, 0.0), &[
                f64x2::new(0.7346051961657583, 0.20298547462974248),
                f64x2::new(0.8166436635290655, 0.7423708925400552),
                f64x2::new(0.16387782224016323, 0.9087068770169618),
            ]);
        }
    }
}