Update nitrokey crate to 0.2.3

This change updates the nitrokey crate to version 0.2.3. This version
bumps the rand crate used to 0.6.1, which in turn requires an additional
set of dependencies.

Import subrepo nitrokey/:nitrokey at b3e2adc5bb1300441ca74cc7672617c042f3ea31
Import subrepo rand/:rand at 73613ff903512e9503e41cc8ba9eae76269dc598
Import subrepo rustc_version/:rustc_version at 0294f2ba2018bf7be672abd53db351ce5055fa02
Import subrepo semver-parser/:semver-parser at 750da9b11a04125231b1fb293866ca036845acee
Import subrepo semver/:semver at 5eb6db94fa03f4d5c64a625a56188f496be47598

1 files changed, 165 insertions, 0 deletions

diff --git a/rand/src/distributions/bernoulli.rs b/rand/src/distributions/bernoulli.rs
new file mode 100644
index 0000000..f49618c
--- /dev/null
+++ b/rand/src/distributions/bernoulli.rs
@@ -0,0 +1,165 @@
+// 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.
+
+//! The Bernoulli distribution.
+
+use Rng;
+use distributions::Distribution;
+
+/// The Bernoulli distribution.
+///
+/// This is a special case of the Binomial distribution where `n = 1`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Bernoulli, Distribution};
+///
+/// let d = Bernoulli::new(0.3);
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a Bernoulli distribution", v);
+/// ```
+///
+/// # Precision
+///
+/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
+/// so only probabilities that are multiples of 2<sup>-64</sup> can be
+/// represented.
+#[derive(Clone, Copy, Debug)]
+pub struct Bernoulli {
+    /// Probability of success, relative to the maximal integer.
+    p_int: u64,
+}
+
+// To sample from the Bernoulli distribution we use a method that compares a
+// random `u64` value `v < (p * 2^64)`.
+//
+// If `p == 1.0`, the integer `v` to compare against can not represented as a
+// `u64`. We manually set it to `u64::MAX` instead (2^64 - 1 instead of 2^64).
+// Note that  value of `p < 1.0` can never result in `u64::MAX`, because an
+// `f64` only has 53 bits of precision, and the next largest value of `p` will
+// result in `2^64 - 2048`.
+//
+// Also there is a 100% theoretical concern: if someone consistenly wants to
+// generate `true` using the Bernoulli distribution (i.e. by using a probability
+// of `1.0`), just using `u64::MAX` is not enough. On average it would return
+// false once every 2^64 iterations. Some people apparently care about this
+// case.
+//
+// That is why we special-case `u64::MAX` to always return `true`, without using
+// the RNG, and pay the performance price for all uses that *are* reasonable.
+// Luckily, if `new()` and `sample` are close, the compiler can optimize out the
+// extra check.
+const ALWAYS_TRUE: u64 = ::core::u64::MAX;
+
+// This is just `2.0.powi(64)`, but written this way because it is not available
+// in `no_std` mode.
+const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
+
+impl Bernoulli {
+    /// Construct a new `Bernoulli` with the given probability of success `p`.
+    ///
+    /// # Panics
+    ///
+    /// If `p < 0` or `p > 1`.
+    ///
+    /// # Precision
+    ///
+    /// For `p = 1.0`, the resulting distribution will always generate true.
+    /// For `p = 0.0`, the resulting distribution will always generate false.
+    ///
+    /// This method is accurate for any input `p` in the range `[0, 1]` which is
+    /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
+    /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
+    #[inline]
+    pub fn new(p: f64) -> Bernoulli {
+        if p < 0.0 || p >= 1.0 {
+            if p == 1.0 { return Bernoulli { p_int: ALWAYS_TRUE } }
+            panic!("Bernoulli::new not called with 0.0 <= p <= 1.0");
+        }
+        Bernoulli { p_int: (p * SCALE) as u64 }
+    }
+
+    /// Construct a new `Bernoulli` with the probability of success of
+    /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
+    /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
+    ///
+    /// If `numerator == denominator` then the returned `Bernoulli` will always
+    /// return `true`. If `numerator == 0` it will always return `false`.
+    ///
+    /// # Panics
+    ///
+    /// If `denominator == 0` or `numerator > denominator`.
+    ///
+    #[inline]
+    pub fn from_ratio(numerator: u32, denominator: u32) -> Bernoulli {
+        assert!(numerator <= denominator);
+        if numerator == denominator {
+            return Bernoulli { p_int: ::core::u64::MAX }
+        }
+        let p_int = ((numerator as f64 / denominator as f64) * SCALE) as u64;
+        Bernoulli { p_int }
+    }
+}
+
+impl Distribution<bool> for Bernoulli {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
+        // Make sure to always return true for p = 1.0.
+        if self.p_int == ALWAYS_TRUE { return true; }
+        let v: u64 = rng.gen();
+        v < self.p_int
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use Rng;
+    use distributions::Distribution;
+    use super::Bernoulli;
+
+    #[test]
+    fn test_trivial() {
+        let mut r = ::test::rng(1);
+        let always_false = Bernoulli::new(0.0);
+        let always_true = Bernoulli::new(1.0);
+        for _ in 0..5 {
+            assert_eq!(r.sample::<bool, _>(&always_false), false);
+            assert_eq!(r.sample::<bool, _>(&always_true), true);
+            assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false);
+            assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true);
+        }
+    }
+
+    #[test]
+    fn test_average() {
+        const P: f64 = 0.3;
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        let d1 = Bernoulli::new(P);
+        let d2 = Bernoulli::from_ratio(NUM, DENOM);
+        const N: u32 = 100_000;
+
+        let mut sum1: u32 = 0;
+        let mut sum2: u32 = 0;
+        let mut rng = ::test::rng(2);
+        for _ in 0..N {
+            if d1.sample(&mut rng) {
+                sum1 += 1;
+            }
+            if d2.sample(&mut rng) {
+                sum2 += 1;
+            }
+        }
+        let avg1 = (sum1 as f64) / (N as f64);
+        assert!((avg1 - P).abs() < 5e-3);
+
+        let avg2 = (sum2 as f64) / (N as f64);
+        assert!((avg2 - (NUM as f64)/(DENOM as f64)).abs() < 5e-3);
+    }
+}