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diff --git a/vendor/rand/src/distributions/mod.rs b/vendor/rand/src/distributions/mod.rs
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--- a/vendor/rand/src/distributions/mod.rs
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-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// http://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Sampling from random distributions.
-//!
-//! This is a generalization of `Rand` to allow parameters to control the
-//! exact properties of the generated values, e.g. the mean and standard
-//! deviation of a normal distribution. The `Sample` trait is the most
-//! general, and allows for generating values that change some state
-//! internally. The `IndependentSample` trait is for generating values
-//! that do not need to record state.
-
-use core::marker;
-
-use {Rng, Rand};
-
-pub use self::range::Range;
-#[cfg(feature="std")]
-pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT};
-#[cfg(feature="std")]
-pub use self::normal::{Normal, LogNormal};
-#[cfg(feature="std")]
-pub use self::exponential::Exp;
-
-pub mod range;
-#[cfg(feature="std")]
-pub mod gamma;
-#[cfg(feature="std")]
-pub mod normal;
-#[cfg(feature="std")]
-pub mod exponential;
-
-#[cfg(feature="std")]
-mod ziggurat_tables;
-
-/// Types that can be used to create a random instance of `Support`.
-pub trait Sample<Support> {
-    /// Generate a random value of `Support`, using `rng` as the
-    /// source of randomness.
-    fn sample<R: Rng>(&mut self, rng: &mut R) -> Support;
-}
-
-/// `Sample`s that do not require keeping track of state.
-///
-/// Since no state is recorded, each sample is (statistically)
-/// independent of all others, assuming the `Rng` used has this
-/// property.
-// FIXME maybe having this separate is overkill (the only reason is to
-// take &self rather than &mut self)? or maybe this should be the
-// trait called `Sample` and the other should be `DependentSample`.
-pub trait IndependentSample<Support>: Sample<Support> {
-    /// Generate a random value.
-    fn ind_sample<R: Rng>(&self, &mut R) -> Support;
-}
-
-/// A wrapper for generating types that implement `Rand` via the
-/// `Sample` & `IndependentSample` traits.
-#[derive(Debug)]
-pub struct RandSample<Sup> {
-    _marker: marker::PhantomData<fn() -> Sup>,
-}
-
-impl<Sup> Copy for RandSample<Sup> {}
-impl<Sup> Clone for RandSample<Sup> {
-    fn clone(&self) -> Self { *self }
-}
-
-impl<Sup: Rand> Sample<Sup> for RandSample<Sup> {
-    fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) }
-}
-
-impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> {
-    fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup {
-        rng.gen()
-    }
-}
-
-impl<Sup> RandSample<Sup> {
-    pub fn new() -> RandSample<Sup> {
-        RandSample { _marker: marker::PhantomData }
-    }
-}
-
-/// A value with a particular weight for use with `WeightedChoice`.
-#[derive(Copy, Clone, Debug)]
-pub struct Weighted<T> {
-    /// The numerical weight of this item
-    pub weight: u32,
-    /// The actual item which is being weighted
-    pub item: T,
-}
-
-/// A distribution that selects from a finite collection of weighted items.
-///
-/// Each item has an associated weight that influences how likely it
-/// is to be chosen: higher weight is more likely.
-///
-/// The `Clone` restriction is a limitation of the `Sample` and
-/// `IndependentSample` traits. Note that `&T` is (cheaply) `Clone` for
-/// all `T`, as is `u32`, so one can store references or indices into
-/// another vector.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Weighted, WeightedChoice, IndependentSample};
-///
-/// let mut items = vec!(Weighted { weight: 2, item: 'a' },
-///                      Weighted { weight: 4, item: 'b' },
-///                      Weighted { weight: 1, item: 'c' });
-/// let wc = WeightedChoice::new(&mut items);
-/// let mut rng = rand::thread_rng();
-/// for _ in 0..16 {
-///      // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
-///      println!("{}", wc.ind_sample(&mut rng));
-/// }
-/// ```
-#[derive(Debug)]
-pub struct WeightedChoice<'a, T:'a> {
-    items: &'a mut [Weighted<T>],
-    weight_range: Range<u32>
-}
-
-impl<'a, T: Clone> WeightedChoice<'a, T> {
-    /// Create a new `WeightedChoice`.
-    ///
-    /// Panics if:
-    ///
-    /// - `items` is empty
-    /// - the total weight is 0
-    /// - the total weight is larger than a `u32` can contain.
-    pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> {
-        // strictly speaking, this is subsumed by the total weight == 0 case
-        assert!(!items.is_empty(), "WeightedChoice::new called with no items");
-
-        let mut running_total: u32 = 0;
-
-        // we convert the list from individual weights to cumulative
-        // weights so we can binary search. This *could* drop elements
-        // with weight == 0 as an optimisation.
-        for item in items.iter_mut() {
-            running_total = match running_total.checked_add(item.weight) {
-                Some(n) => n,
-                None => panic!("WeightedChoice::new called with a total weight \
-                               larger than a u32 can contain")
-            };
-
-            item.weight = running_total;
-        }
-        assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0");
-
-        WeightedChoice {
-            items: items,
-            // we're likely to be generating numbers in this range
-            // relatively often, so might as well cache it
-            weight_range: Range::new(0, running_total)
-        }
-    }
-}
-
-impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> {
-    fn sample<R: Rng>(&mut self, rng: &mut R) -> T { self.ind_sample(rng) }
-}
-
-impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> {
-    fn ind_sample<R: Rng>(&self, rng: &mut R) -> T {
-        // we want to find the first element that has cumulative
-        // weight > sample_weight, which we do by binary since the
-        // cumulative weights of self.items are sorted.
-
-        // choose a weight in [0, total_weight)
-        let sample_weight = self.weight_range.ind_sample(rng);
-
-        // short circuit when it's the first item
-        if sample_weight < self.items[0].weight {
-            return self.items[0].item.clone();
-        }
-
-        let mut idx = 0;
-        let mut modifier = self.items.len();
-
-        // now we know that every possibility has an element to the
-        // left, so we can just search for the last element that has
-        // cumulative weight <= sample_weight, then the next one will
-        // be "it". (Note that this greatest element will never be the
-        // last element of the vector, since sample_weight is chosen
-        // in [0, total_weight) and the cumulative weight of the last
-        // one is exactly the total weight.)
-        while modifier > 1 {
-            let i = idx + modifier / 2;
-            if self.items[i].weight <= sample_weight {
-                // we're small, so look to the right, but allow this
-                // exact element still.
-                idx = i;
-                // we need the `/ 2` to round up otherwise we'll drop
-                // the trailing elements when `modifier` is odd.
-                modifier += 1;
-            } else {
-                // otherwise we're too big, so go left. (i.e. do
-                // nothing)
-            }
-            modifier /= 2;
-        }
-        return self.items[idx + 1].item.clone();
-    }
-}
-
-/// Sample a random number using the Ziggurat method (specifically the
-/// ZIGNOR variant from Doornik 2005). Most of the arguments are
-/// directly from the paper:
-///
-/// * `rng`: source of randomness
-/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0.
-/// * `X`: the $x_i$ abscissae.
-/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$)
-/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
-/// * `pdf`: the probability density function
-/// * `zero_case`: manual sampling from the tail when we chose the
-///    bottom box (i.e. i == 0)
-
-// the perf improvement (25-50%) is definitely worth the extra code
-// size from force-inlining.
-#[cfg(feature="std")]
-#[inline(always)]
-fn ziggurat<R: Rng, P, Z>(
-            rng: &mut R,
-            symmetric: bool,
-            x_tab: ziggurat_tables::ZigTable,
-            f_tab: ziggurat_tables::ZigTable,
-            mut pdf: P,
-            mut zero_case: Z)
-            -> f64 where P: FnMut(f64) -> f64, Z: FnMut(&mut R, f64) -> f64 {
-    const SCALE: f64 = (1u64 << 53) as f64;
-    loop {
-        // reimplement the f64 generation as an optimisation suggested
-        // by the Doornik paper: we have a lot of precision-space
-        // (i.e. there are 11 bits of the 64 of a u64 to use after
-        // creating a f64), so we might as well reuse some to save
-        // generating a whole extra random number. (Seems to be 15%
-        // faster.)
-        //
-        // This unfortunately misses out on the benefits of direct
-        // floating point generation if an RNG like dSMFT is
-        // used. (That is, such RNGs create floats directly, highly
-        // efficiently and overload next_f32/f64, so by not calling it
-        // this may be slower than it would be otherwise.)
-        // FIXME: investigate/optimise for the above.
-        let bits: u64 = rng.gen();
-        let i = (bits & 0xff) as usize;
-        let f = (bits >> 11) as f64 / SCALE;
-
-        // u is either U(-1, 1) or U(0, 1) depending on if this is a
-        // symmetric distribution or not.
-        let u = if symmetric {2.0 * f - 1.0} else {f};
-        let x = u * x_tab[i];
-
-        let test_x = if symmetric { x.abs() } else {x};
-
-        // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i])
-        if test_x < x_tab[i + 1] {
-            return x;
-        }
-        if i == 0 {
-            return zero_case(rng, u);
-        }
-        // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
-        if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) {
-            return x;
-        }
-    }
-}
-
-#[cfg(test)]
-mod tests {
-
-    use {Rng, Rand};
-    use super::{RandSample, WeightedChoice, Weighted, Sample, IndependentSample};
-
-    #[derive(PartialEq, Debug)]
-    struct ConstRand(usize);
-    impl Rand for ConstRand {
-        fn rand<R: Rng>(_: &mut R) -> ConstRand {
-            ConstRand(0)
-        }
-    }
-
-    // 0, 1, 2, 3, ...
-    struct CountingRng { i: u32 }
-    impl Rng for CountingRng {
-        fn next_u32(&mut self) -> u32 {
-            self.i += 1;
-            self.i - 1
-        }
-        fn next_u64(&mut self) -> u64 {
-            self.next_u32() as u64
-        }
-    }
-
-    #[test]
-    fn test_rand_sample() {
-        let mut rand_sample = RandSample::<ConstRand>::new();
-
-        assert_eq!(rand_sample.sample(&mut ::test::rng()), ConstRand(0));
-        assert_eq!(rand_sample.ind_sample(&mut ::test::rng()), ConstRand(0));
-    }
-    #[test]
-    fn test_weighted_choice() {
-        // this makes assumptions about the internal implementation of
-        // WeightedChoice, specifically: it doesn't reorder the items,
-        // it doesn't do weird things to the RNG (so 0 maps to 0, 1 to
-        // 1, internally; modulo a modulo operation).
-
-        macro_rules! t {
-            ($items:expr, $expected:expr) => {{
-                let mut items = $items;
-                let wc = WeightedChoice::new(&mut items);
-                let expected = $expected;
-
-                let mut rng = CountingRng { i: 0 };
-
-                for &val in expected.iter() {
-                    assert_eq!(wc.ind_sample(&mut rng), val)
-                }
-            }}
-        }
-
-        t!(vec!(Weighted { weight: 1, item: 10}), [10]);
-
-        // skip some
-        t!(vec!(Weighted { weight: 0, item: 20},
-                Weighted { weight: 2, item: 21},
-                Weighted { weight: 0, item: 22},
-                Weighted { weight: 1, item: 23}),
-           [21,21, 23]);
-
-        // different weights
-        t!(vec!(Weighted { weight: 4, item: 30},
-                Weighted { weight: 3, item: 31}),
-           [30,30,30,30, 31,31,31]);
-
-        // check that we're binary searching
-        // correctly with some vectors of odd
-        // length.
-        t!(vec!(Weighted { weight: 1, item: 40},
-                Weighted { weight: 1, item: 41},
-                Weighted { weight: 1, item: 42},
-                Weighted { weight: 1, item: 43},
-                Weighted { weight: 1, item: 44}),
-           [40, 41, 42, 43, 44]);
-        t!(vec!(Weighted { weight: 1, item: 50},
-                Weighted { weight: 1, item: 51},
-                Weighted { weight: 1, item: 52},
-                Weighted { weight: 1, item: 53},
-                Weighted { weight: 1, item: 54},
-                Weighted { weight: 1, item: 55},
-                Weighted { weight: 1, item: 56}),
-           [50, 51, 52, 53, 54, 55, 56]);
-    }
-
-    #[test]
-    fn test_weighted_clone_initialization() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let clone = initial.clone();
-        assert_eq!(initial.weight, clone.weight);
-        assert_eq!(initial.item, clone.item);
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_clone_change_weight() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let mut clone = initial.clone();
-        clone.weight = 5;
-        assert_eq!(initial.weight, clone.weight);
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_clone_change_item() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let mut clone = initial.clone();
-        clone.item = 5;
-        assert_eq!(initial.item, clone.item);
-
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_choice_no_items() {
-        WeightedChoice::<isize>::new(&mut []);
-    }
-    #[test] #[should_panic]
-    fn test_weighted_choice_zero_weight() {
-        WeightedChoice::new(&mut [Weighted { weight: 0, item: 0},
-                                  Weighted { weight: 0, item: 1}]);
-    }
-    #[test] #[should_panic]
-    fn test_weighted_choice_weight_overflows() {
-        let x = ::std::u32::MAX / 2; // x + x + 2 is the overflow
-        WeightedChoice::new(&mut [Weighted { weight: x, item: 0 },
-                                  Weighted { weight: 1, item: 1 },
-                                  Weighted { weight: x, item: 2 },
-                                  Weighted { weight: 1, item: 3 }]);
-    }
-}