Fix the implementation of `std::rand::Rng::fill_bytes()` for
`std::rand::reseeding::ReseedingRng` to call the `fill_bytes()` method
of the underlying RNG rather than itself, which causes infinite
recursion.
Fixes#10202.
Fix the implementation of `std::rand::Rng::fill_bytes()` for
`std::rand::reseeding::ReseedingRng` to call the `fill_bytes()` method
of the underlying RNG rather than itself, which causes infinite
recursion.
Fixes#10202.
The code was using (in the notation of Doornik 2005) `f(x_{i+1}) -
f(x_{i+2})` rather than `f(x_i) - f(x_{i+1})`. This corrects that, and
removes the F_DIFF tables which caused this problem in the first place.
They `F_DIFF` tables are a micro-optimisation (in theory, they could
easily be a micro-pessimisation): that `if` gets hit about 1% of the
time for Exp/Normal, and the rest of the condition involves RNG calls
and a floating point `exp`, so it is unlikely that saving a single FP
subtraction will be very useful (especially as more tables means more
memory reads and higher cache pressure, as well as taking up space in
the binary (although only ~2k in this case)).
Closes#10084. Notably, unlike that issue suggests, this wasn't a
problem with the Exp tables. It affected Normal too, but since it is
symmetric, there was no bias in the mean (as the bias was equal on the
positive and negative sides and so cancelled out) but it was visible as
a variance slightly lower than it should be.
- Adds the `Sample` and `IndependentSample` traits for generating numbers where there are parameters (e.g. a list of elements to draw from, or the mean/variance of a normal distribution). The former takes `&mut self` and the latter takes `&self` (this is the only difference).
- Adds proper `Normal` and `Exp`-onential distributions
- Adds `Range` which generates `[lo, hi)` generically & properly (via a new trait) replacing the incorrect behaviour of `Rng.gen_integer_range` (this has become `Rng.gen_range` for convenience, it's far more efficient to use `Range` itself)
- Move the `Weighted` struct from `std::rand` to `std::rand::distributions` & improve it
- optimisations and docs
Slice transmutes are now (and, really, always were) dangerous, so we
avoid them and do the (only?) non-(undefined behaviour in C) pointer
cast: casting to *u8.
This reifies the computations required for uniformity done by
(the old) `Rng.gen_integer_range` (now Rng.gen_range), so that they can
be amortised over many invocations, if it is called in a loop.
Also, it makes it correct, but using a trait + impls for each type,
rather than trying to coerce `Int` + `u64` to do the right thing. This
also makes it more extensible, e.g. big integers could & should
implement SampleRange.
Complete the implementation of Exp and Normal started by Exp1 and
StandardNormal by creating types implementing Sample & IndependentSample
with the appropriate parameters.
This lets the C++ code in the rt handle the (slightly) tricky parts of
random number generation: e.g. error detection/handling, and using the
values of the `#define`d options to the various functions.
It now:
- can be explicitly seeded from user code (`seed_task_rng`) or from the
environment (`RUST_SEED`, a positive integer)
- automatically reseeds itself from the OS *unless* it was seeded by
either method above
- has more documentation
This provides 2 methods: .reseed() and ::from_seed that modify and
create respecitively.
Implement this trait for the RNGs in the stdlib for which this makes
sense.
This is implemented as a wrapper around another RNG. It is designed
to allow the actual implementation to be changed without changing
the external API (e.g. it currently uses a 64-bit generator on 64-
bit platforms, and a 32-bit one on 32-bit platforms; but one could
imagine that the IsaacRng may be deprecated later, and having this
ability to switch algorithms without having to update the points of
use is convenient.)
This is the recommended general use RNG.
The former reads from e.g. /dev/urandom, the latter just wraps any
std::rt::io::Reader into an interface that implements Rng.
This also adds Rng.fill_bytes for efficient implementations of the above
(reading 8 bytes at a time is inefficient when you can read 1000), and
removes the dependence on src/rt (i.e. rand_gen_seed) although this last
one requires implementing hand-seeding of the XorShiftRng used in the
scheduler on Linux/unixes, since OSRng relies on a scheduler existing to
be able to read from /dev/urandom.
This is 2x faster on 64-bit computers at generating anything larger
than 32-bits.
It has been verified against the canonical C implementation from the
website of the creator of ISAAC64.
Also, move `Rng.next` to `Rng.next_u32` and add `Rng.next_u64` to
take full advantage of the wider word width; otherwise Isaac64 will
always be squeezed down into a u32 wasting half the entropy and
offering no advantage over the 32-bit variant.
This PR solves one of the pain points with c-style enums. Simplifies writing a fn to convert from an int/uint to an enum. It does this through a `#[deriving(FromPrimitive)]` syntax extension.
Before this is committed though, we need to discuss if `ToPrimitive`/`FromPrimitive` has the right design (cc #4819). I've changed all the `.to_int()` and `from_int()` style functions to return `Option<int>` so we can handle partial functions. For this PR though only enums and `extra::num::bigint::*` take advantage of returning None for unrepresentable values. In the long run it'd be better if `i64.to_i8()` returned `None` if the value was too large, but I'll save this for a future PR.
Closes#3868.
