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Diffstat (limited to 'vendor/rand')
33 files changed, 0 insertions, 7552 deletions
diff --git a/vendor/rand/.cargo-checksum.json b/vendor/rand/.cargo-checksum.json deleted file mode 100644 index 56e7916..0000000 --- a/vendor/rand/.cargo-checksum.json +++ /dev/null @@ -1 +0,0 @@ -{"files":{"CHANGELOG.md":"f5e9c71d4123971f9f6c54eed8837245e6cac4b610c9d23b680fa95538946142","Cargo.toml":"4c770ee471e19f4a453a36d76479f9a530987058b793c735cd7a2bcf937d7edb","LICENSE-APACHE":"a60eea817514531668d7e00765731449fe14d059d3249e0bc93b36de45f759f2","LICENSE-MIT":"6485b8ed310d3f0340bf1ad1f47645069ce4069dcc6bb46c7d5c6faf41de1fdb","README.md":"fb8071c3bc1013107b16ebcb303f31ef614e81440f2d58a46bfb9ff1e311b792","appveyor.yml":"8796156caf7041ef2a43f7a313df21ea639de3f2563b6181bba1096b1c489f1b","benches/bench.rs":"35c4ab609f2a5f5aab6c52c257415258dc0780621b492b5a82bb12d048cab6db","benches/distributions/exponential.rs":"99cb59c013a0b6bb390d34c5649b341fc3b88ea7df0caf2470bdda8798f9fe3d","benches/distributions/gamma.rs":"3533f311e4b55d743c5b01a7eb6529c94fd97726ef6702a6372f914f5f33666b","benches/distributions/mod.rs":"0028f1cb96f61152ed5b49a4fe91227d809ef6d19035592c36032a538af7f95e","benches/distributions/normal.rs":"4e10c18cb583ccb96301ea953c8e0aa9ee3b6662060271d1b8d19ca23364dc6b","benches/generators.rs":"aaa2f1dbfb399df8323d8a5796b92add6210cd5f0f1d916895ffdd81d60f812b","benches/misc.rs":"bd2f7c5a16f0fcb59022d5aeef66ed3c94e89ebf6c06667851dd23d0b1595504","src/distributions/exponential.rs":"103c8412c8a581b71835f1c00e40f6370e7702adf9d499243933a793d132d4e7","src/distributions/gamma.rs":"7a3f85c8daad4e56e334586ddb9fc9d83df3b0699738ed681a6c41e4ed455be9","src/distributions/mod.rs":"7943c4f83721bac816f831cca3b1574b6136932f7b4927aa6101130080ba62c5","src/distributions/normal.rs":"1562b43f80e4d5f83a8deb5af18de5a18dfeeeeda11fefc577da26672b14c949","src/distributions/range.rs":"a72a538d3ec4ed23f8d632aa55fd4793c464f24a5872d04ce8095ddd5db92115","src/distributions/ziggurat_tables.rs":"4eacf94fc352c91c455a6623de6a721e53842e1690f13a5662b6a79c7fbb73de","src/jitter.rs":"befd4b84bf753c107370b5b9498ad49611c220bdae2e4be9ee4398e9fa497042","src/lib.rs":"f9f4d15c2ce67f9ba21261a4bc76599523b930698cee2ae1e37d01f0d2ba834e","src/os.rs":"bbba4481432ae0f19bafb2168af5e7e1a858547ff8a7f8996286ea1b2a951158","src/prng/chacha.rs":"558007276f9c22933d39e5b8e853f4dd9533e823ed66df8dc1f23ad6925b1d51","src/prng/isaac.rs":"a8a2ee8b38d312663308e3bdf03376e342fd91330655f39144e5bba7392b2a8e","src/prng/isaac64.rs":"f28f7596ccab910db265b42671116abb9d2039fa8a421cbc75312bd0e7715d3a","src/prng/mod.rs":"c1a73450f49e819a20942a5b591f84a08ebb5ac33aa0f65b18ac1dc9a19a3084","src/prng/xorshift.rs":"606c308747293652c868b46dc3cad847d0c3717629c04ba75681c887c7634114","src/rand_impls.rs":"e1f27077fc13d5855bb66235f8ccfb216e116337eb38424d9c30c090e112215c","src/read.rs":"bd0eb508a6b659dc578d546fc2f231484aed80c73cfe8c475e0d65c8d699a769","src/reseeding.rs":"a97b86387b87ea1adc5262ddea480fe735c9c2a86762abaace29119022ac9f6e","src/seq.rs":"76dd58af0f580aed2721c393a5c036322186dc7cb3b4abed33436620c7c49288","utils/ziggurat_tables.py":"a9fc0a2fdae9b5c798c238788f94b720c156e13fd96f2356c409aa533191eb94"},"package":"552840b97013b1a26992c11eac34bdd778e464601a4c2054b5f0bff7c6761293"}
\ No newline at end of file diff --git a/vendor/rand/CHANGELOG.md b/vendor/rand/CHANGELOG.md deleted file mode 100644 index aa4c293..0000000 --- a/vendor/rand/CHANGELOG.md +++ /dev/null @@ -1,277 +0,0 @@ -# Changelog -All notable changes to this project will be documented in this file. - -The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) -and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). - -## [0.4.5] - 2019-01-25 -### Platforms -- Fuchsia: Replaced fuchsia-zircon with fuchsia-cprng - -## [0.4.4] - 2019-01-06 -### Added -- SGX support - -## [0.4.3] - 2018-08-16 -### Fixed -- Use correct syscall number for PowerPC (#589) - -## [0.4.2] - 2018-01-05 -### Changed -- Use winapi on Windows -- Update for Fuchsia OS -- Remove dev-dependency on `log` - -## [0.4.1] - 2017-12-17 -### Added -- `no_std` support - -## [0.4.0-pre.0] - 2017-12-11 -### Added -- `JitterRng` added as a high-quality alternative entropy source using the - system timer -- new `seq` module with `sample_iter`, `sample_slice`, etc. -- WASM support via dummy implementations (fail at run-time) -- Additional benchmarks, covering generators and new seq code - -### Changed -- `thread_rng` uses `JitterRng` if seeding from system time fails - (slower but more secure than previous method) - -### Deprecated - - `sample` function deprecated (replaced by `sample_iter`) - -## [0.3.18] - 2017-11-06 -### Changed -- `thread_rng` is seeded from the system time if `OsRng` fails -- `weak_rng` now uses `thread_rng` internally - - -## [0.3.17] - 2017-10-07 -### Changed - - Fuchsia: Magenta was renamed Zircon - -## [0.3.16] - 2017-07-27 -### Added -- Implement Debug for mote non-public types -- implement `Rand` for (i|u)i128 -- Support for Fuchsia - -### Changed -- Add inline attribute to SampleRange::construct_range. - This improves the benchmark for sample in 11% and for shuffle in 16%. -- Use `RtlGenRandom` instead of `CryptGenRandom` - - -## [0.3.15] - 2016-11-26 -### Added -- Add `Rng` trait method `choose_mut` -- Redox support - -### Changed -- Use `arc4rand` for `OsRng` on FreeBSD. -- Use `arc4random(3)` for `OsRng` on OpenBSD. - -### Fixed -- Fix filling buffers 4 GiB or larger with `OsRng::fill_bytes` on Windows - - -## [0.3.14] - 2016-02-13 -### Fixed -- Inline definitions from winapi/advapi32, wich decreases build times - - -## [0.3.13] - 2016-01-09 -### Fixed -- Compatible with Rust 1.7.0-nightly (needed some extra type annotations) - - -## [0.3.12] - 2015-11-09 -### Changed -- Replaced the methods in `next_f32` and `next_f64` with the technique described - Saito & Matsumoto at MCQMC'08. The new method should exhibit a slightly more - uniform distribution. -- Depend on libc 0.2 - -### Fixed -- Fix iterator protocol issue in `rand::sample` - - -## [0.3.11] - 2015-08-31 -### Added -- Implement `Rand` for arrays with n <= 32 - - -## [0.3.10] - 2015-08-17 -### Added -- Support for NaCl platforms - -### Changed -- Allow `Rng` to be `?Sized`, impl for `&mut R` and `Box<R>` where `R: ?Sized + Rng` - - -## [0.3.9] - 2015-06-18 -### Changed -- Use `winapi` for Windows API things - -### Fixed -- Fixed test on stable/nightly -- Fix `getrandom` syscall number for aarch64-unknown-linux-gnu - - -## [0.3.8] - 2015-04-23 -### Changed -- `log` is a dev dependency - -### Fixed -- Fix race condition of atomics in `is_getrandom_available` - - -## [0.3.7] - 2015-04-03 -### Fixed -- Derive Copy/Clone changes - - -## [0.3.6] - 2015-04-02 -### Changed -- Move to stable Rust! - - -## [0.3.5] - 2015-04-01 -### Fixed -- Compatible with Rust master - - -## [0.3.4] - 2015-03-31 -### Added -- Implement Clone for `Weighted` - -### Fixed -- Compatible with Rust master - - -## [0.3.3] - 2015-03-26 -### Fixed -- Fix compile on Windows - - -## [0.3.2] - 2015-03-26 - - -## [0.3.1] - 2015-03-26 -### Fixed -- Fix compile on Windows - - -## [0.3.0] - 2015-03-25 -### Changed -- Update to use log version 0.3.x - - -## [0.2.1] - 2015-03-22 -### Fixed -- Compatible with Rust master -- Fixed iOS compilation - - -## [0.2.0] - 2015-03-06 -### Fixed -- Compatible with Rust master (move from `old_io` to `std::io`) - - -## [0.1.4] - 2015-03-04 -### Fixed -- Compatible with Rust master (use wrapping ops) - - -## [0.1.3] - 2015-02-20 -### Fixed -- Compatible with Rust master - -### Removed -- Removed Copy inplementaions from RNGs - - -## [0.1.2] - 2015-02-03 -### Added -- Imported functionality from `std::rand`, including: - - `StdRng`, `SeedableRng`, `TreadRng`, `weak_rng()` - - `ReaderRng`: A wrapper around any Reader to treat it as an RNG. -- Imported documentation from `std::rand` -- Imported tests from `std::rand` - - -## [0.1.1] - 2015-02-03 -### Added -- Migrate to a cargo-compatible directory structure. - -### Fixed -- Do not use entropy during `gen_weighted_bool(1)` - - -## [Rust 0.12.0] - 2014-10-09 -### Added -- Impl Rand for tuples of arity 11 and 12 -- Include ChaCha pseudorandom generator -- Add `next_f64` and `next_f32` to Rng -- Implement Clone for PRNGs - -### Changed -- Rename `TaskRng` to `ThreadRng` and `task_rng` to `thread_rng` (since a - runtime is removed from Rust). - -### Fixed -- Improved performance of ISAAC and ISAAC64 by 30% and 12 % respectively, by - informing the optimiser that indexing is never out-of-bounds. - -### Removed -- Removed the Deprecated `choose_option` - - -## [Rust 0.11.0] - 2014-07-02 -### Added -- document when to use `OSRng` in cryptographic context, and explain why we use `/dev/urandom` instead of `/dev/random` -- `Rng::gen_iter()` which will return an infinite stream of random values -- `Rng::gen_ascii_chars()` which will return an infinite stream of random ascii characters - -### Changed -- Now only depends on libcore! 2adf5363f88ffe06f6d2ea5c338d1b186d47f4a1 -- Remove `Rng.choose()`, rename `Rng.choose_option()` to `.choose()` -- Rename OSRng to OsRng -- The WeightedChoice structure is no longer built with a `Vec<Weighted<T>>`, - but rather a `&mut [Weighted<T>]`. This means that the WeightedChoice - structure now has a lifetime associated with it. -- The `sample` method on `Rng` has been moved to a top-level function in the - `rand` module due to its dependence on `Vec`. - -### Removed -- `Rng::gen_vec()` was removed. Previous behavior can be regained with - `rng.gen_iter().take(n).collect()` -- `Rng::gen_ascii_str()` was removed. Previous behavior can be regained with - `rng.gen_ascii_chars().take(n).collect()` -- {IsaacRng, Isaac64Rng, XorShiftRng}::new() have all been removed. These all - relied on being able to use an OSRng for seeding, but this is no longer - available in librand (where these types are defined). To retain the same - functionality, these types now implement the `Rand` trait so they can be - generated with a random seed from another random number generator. This allows - the stdlib to use an OSRng to create seeded instances of these RNGs. -- Rand implementations for `Box<T>` and `@T` were removed. These seemed to be - pretty rare in the codebase, and it allows for librand to not depend on - liballoc. Additionally, other pointer types like Rc<T> and Arc<T> were not - supported. -- Remove a slew of old deprecated functions - - -## [Rust 0.10] - 2014-04-03 -### Changed -- replace `Rng.shuffle's` functionality with `.shuffle_mut` -- bubble up IO errors when creating an OSRng - -### Fixed -- Use `fill()` instead of `read()` -- Rewrite OsRng in Rust for windows - -## [0.10-pre] - 2014-03-02 -### Added -- Seperate `rand` out of the standard library - diff --git a/vendor/rand/Cargo.toml b/vendor/rand/Cargo.toml deleted file mode 100644 index 64d240c..0000000 --- a/vendor/rand/Cargo.toml +++ /dev/null @@ -1,45 +0,0 @@ -# THIS FILE IS AUTOMATICALLY GENERATED BY CARGO -# -# When uploading crates to the registry Cargo will automatically -# "normalize" Cargo.toml files for maximal compatibility -# with all versions of Cargo and also rewrite `path` dependencies -# to registry (e.g. crates.io) dependencies -# -# If you believe there's an error in this file please file an -# issue against the rust-lang/cargo repository. If you're -# editing this file be aware that the upstream Cargo.toml -# will likely look very different (and much more reasonable) - -[package] -name = "rand" -version = "0.4.6" -authors = ["The Rust Project Developers"] -description = "Random number generators and other randomness functionality.\n" -homepage = "https://github.com/rust-lang-nursery/rand" -documentation = "https://docs.rs/rand" -readme = "README.md" -keywords = ["random", "rng"] -categories = ["algorithms"] -license = "MIT/Apache-2.0" -repository = "https://github.com/rust-lang-nursery/rand" - -[features] -alloc = [] -default = ["std"] -i128_support = [] -nightly = ["i128_support"] -std = ["libc"] -[target."cfg(target_env = \"sgx\")".dependencies.rand_core] -version = "0.3" -default-features = false - -[target."cfg(target_env = \"sgx\")".dependencies.rdrand] -version = "0.4.0" -[target."cfg(target_os = \"fuchsia\")".dependencies.fuchsia-cprng] -version = "0.1.0" -[target."cfg(unix)".dependencies.libc] -version = "0.2" -optional = true -[target."cfg(windows)".dependencies.winapi] -version = "0.3" -features = ["minwindef", "ntsecapi", "profileapi", "winnt"] diff --git a/vendor/rand/LICENSE-APACHE b/vendor/rand/LICENSE-APACHE deleted file mode 100644 index 16fe87b..0000000 --- a/vendor/rand/LICENSE-APACHE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - -TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - -1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - -Copyright [yyyy] [name of copyright owner] - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. diff --git a/vendor/rand/LICENSE-MIT b/vendor/rand/LICENSE-MIT deleted file mode 100644 index 39d4bdb..0000000 --- a/vendor/rand/LICENSE-MIT +++ /dev/null @@ -1,25 +0,0 @@ -Copyright (c) 2014 The Rust Project Developers - -Permission is hereby granted, free of charge, to any -person obtaining a copy of this software and associated -documentation files (the "Software"), to deal in the -Software without restriction, including without -limitation the rights to use, copy, modify, merge, -publish, distribute, sublicense, and/or sell copies of -the Software, and to permit persons to whom the Software -is furnished to do so, subject to the following -conditions: - -The above copyright notice and this permission notice -shall be included in all copies or substantial portions -of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF -ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED -TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A -PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT -SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY -CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION -OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR -IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER -DEALINGS IN THE SOFTWARE. diff --git a/vendor/rand/README.md b/vendor/rand/README.md deleted file mode 100644 index f72bd51..0000000 --- a/vendor/rand/README.md +++ /dev/null @@ -1,139 +0,0 @@ -rand -==== - -A Rust library for random number generators and other randomness functionality. - -[](https://travis-ci.org/rust-lang-nursery/rand) -[](https://ci.appveyor.com/project/alexcrichton/rand) - -[Documentation](https://docs.rs/rand) - -## Usage - -Add this to your `Cargo.toml`: - -```toml -[dependencies] -rand = "0.4" -``` - -and this to your crate root: - -```rust -extern crate rand; -``` - -### Versions - -Version `0.4`was released in December 2017. It contains almost no breaking -changes since the `0.3` series, but nevertheless contains some significant -new code, including a new "external" entropy source (`JitterRng`) and `no_std` -support. - -Version `0.5` is in development and contains significant performance -improvements for the ISAAC random number generators. - -## Examples - -There is built-in support for a random number generator (RNG) associated with each thread stored in thread-local storage. This RNG can be accessed via thread_rng, or used implicitly via random. This RNG is normally randomly seeded from an operating-system source of randomness, e.g. /dev/urandom on Unix systems, and will automatically reseed itself from this source after generating 32 KiB of random data. - -```rust -let tuple = rand::random::<(f64, char)>(); -println!("{:?}", tuple) -``` - -```rust -use rand::Rng; - -let mut rng = rand::thread_rng(); -if rng.gen() { // random bool - println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>()) -} -``` - -It is also possible to use other RNG types, which have a similar interface. The following uses the "ChaCha" algorithm instead of the default. - -```rust -use rand::{Rng, ChaChaRng}; - -let mut rng = rand::ChaChaRng::new_unseeded(); -println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>()) -``` - -## Features - -By default, `rand` is built with all stable features available. The following -optional features are available: - -- `i128_support` enables support for generating `u128` and `i128` values -- `nightly` enables all unstable features (`i128_support`) -- `std` enabled by default; by setting "default-features = false" `no_std` - mode is activated; this removes features depending on `std` functionality: - - - `OsRng` is entirely unavailable - - `JitterRng` code is still present, but a nanosecond timer must be - provided via `JitterRng::new_with_timer` - - Since no external entropy is available, it is not possible to create - generators with fresh seeds (user must provide entropy) - - `thread_rng`, `weak_rng` and `random` are all disabled - - exponential, normal and gamma type distributions are unavailable - since `exp` and `log` functions are not provided in `core` - - any code requiring `Vec` or `Box` -- `alloc` can be used instead of `std` to provide `Vec` and `Box` - -## Testing - -Unfortunately, `cargo test` does not test everything. The following tests are -recommended: - -``` -# Basic tests for rand and sub-crates -cargo test --all - -# Test no_std support (build only since nearly all tests require std) -cargo build --all --no-default-features - -# Test 128-bit support (requires nightly) -cargo test --all --features nightly - -# Benchmarks (requires nightly) -cargo bench -# or just to test the benchmark code: -cargo test --benches -``` - -# `derive(Rand)` - -You can derive the `Rand` trait for your custom type via the `#[derive(Rand)]` -directive. To use this first add this to your Cargo.toml: - -```toml -rand = "0.4" -rand_derive = "0.3" -``` - -Next in your crate: - -```rust -extern crate rand; -#[macro_use] -extern crate rand_derive; - -#[derive(Rand, Debug)] -struct MyStruct { - a: i32, - b: u32, -} - -fn main() { - println!("{:?}", rand::random::<MyStruct>()); -} -``` - - -# License - -`rand` is primarily distributed under the terms of both the MIT -license and the Apache License (Version 2.0). - -See LICENSE-APACHE, and LICENSE-MIT for details. diff --git a/vendor/rand/appveyor.yml b/vendor/rand/appveyor.yml deleted file mode 100644 index 02e217f..0000000 --- a/vendor/rand/appveyor.yml +++ /dev/null @@ -1,38 +0,0 @@ -environment: - - # At the time this was added AppVeyor was having troubles with checking - # revocation of SSL certificates of sites like static.rust-lang.org and what - # we think is crates.io. The libcurl HTTP client by default checks for - # revocation on Windows and according to a mailing list [1] this can be - # disabled. - # - # The `CARGO_HTTP_CHECK_REVOKE` env var here tells cargo to disable SSL - # revocation checking on Windows in libcurl. Note, though, that rustup, which - # we're using to download Rust here, also uses libcurl as the default backend. - # Unlike Cargo, however, rustup doesn't have a mechanism to disable revocation - # checking. To get rustup working we set `RUSTUP_USE_HYPER` which forces it to - # use the Hyper instead of libcurl backend. Both Hyper and libcurl use - # schannel on Windows but it appears that Hyper configures it slightly - # differently such that revocation checking isn't turned on by default. - # - # [1]: https://curl.haxx.se/mail/lib-2016-03/0202.html - RUSTUP_USE_HYPER: 1 - CARGO_HTTP_CHECK_REVOKE: false - - matrix: - - TARGET: x86_64-pc-windows-msvc - - TARGET: i686-pc-windows-msvc -install: - - appveyor DownloadFile https://win.rustup.rs/ -FileName rustup-init.exe - - rustup-init.exe -y --default-host %TARGET% --default-toolchain nightly - - set PATH=%PATH%;C:\Users\appveyor\.cargo\bin - - rustc -V - - cargo -V - -build: false - -test_script: - - cargo test --benches - - cargo test - - cargo test --features nightly - - cargo test --manifest-path rand-derive/Cargo.toml diff --git a/vendor/rand/benches/bench.rs b/vendor/rand/benches/bench.rs deleted file mode 100644 index d396f25..0000000 --- a/vendor/rand/benches/bench.rs +++ /dev/null @@ -1,34 +0,0 @@ -#![feature(test)] - -extern crate test; -extern crate rand; - -const RAND_BENCH_N: u64 = 1000; - -mod distributions; - -use std::mem::size_of; -use test::{black_box, Bencher}; -use rand::{StdRng, Rng}; - -#[bench] -fn rand_f32(b: &mut Bencher) { - let mut rng = StdRng::new().unwrap(); - b.iter(|| { - for _ in 0..RAND_BENCH_N { - black_box(rng.next_f32()); - } - }); - b.bytes = size_of::<f32>() as u64 * RAND_BENCH_N; -} - -#[bench] -fn rand_f64(b: &mut Bencher) { - let mut rng = StdRng::new().unwrap(); - b.iter(|| { - for _ in 0..RAND_BENCH_N { - black_box(rng.next_f64()); - } - }); - b.bytes = size_of::<f64>() as u64 * RAND_BENCH_N; -} diff --git a/vendor/rand/benches/distributions/exponential.rs b/vendor/rand/benches/distributions/exponential.rs deleted file mode 100644 index 152615d..0000000 --- a/vendor/rand/benches/distributions/exponential.rs +++ /dev/null @@ -1,18 +0,0 @@ -use std::mem::size_of; -use test::Bencher; -use rand; -use rand::distributions::exponential::Exp; -use rand::distributions::Sample; - -#[bench] -fn rand_exp(b: &mut Bencher) { - let mut rng = rand::weak_rng(); - let mut exp = Exp::new(2.71828 * 3.14159); - - b.iter(|| { - for _ in 0..::RAND_BENCH_N { - exp.sample(&mut rng); - } - }); - b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N; -} diff --git a/vendor/rand/benches/distributions/gamma.rs b/vendor/rand/benches/distributions/gamma.rs deleted file mode 100644 index bf3fd36..0000000 --- a/vendor/rand/benches/distributions/gamma.rs +++ /dev/null @@ -1,31 +0,0 @@ -use std::mem::size_of; -use test::Bencher; -use rand; -use rand::distributions::IndependentSample; -use rand::distributions::gamma::Gamma; - -#[bench] -fn bench_gamma_large_shape(b: &mut Bencher) { - let gamma = Gamma::new(10., 1.0); - let mut rng = rand::weak_rng(); - - b.iter(|| { - for _ in 0..::RAND_BENCH_N { - gamma.ind_sample(&mut rng); - } - }); - b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N; -} - -#[bench] -fn bench_gamma_small_shape(b: &mut Bencher) { - let gamma = Gamma::new(0.1, 1.0); - let mut rng = rand::weak_rng(); - - b.iter(|| { - for _ in 0..::RAND_BENCH_N { - gamma.ind_sample(&mut rng); - } - }); - b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N; -} diff --git a/vendor/rand/benches/distributions/mod.rs b/vendor/rand/benches/distributions/mod.rs deleted file mode 100644 index 49f6bd9..0000000 --- a/vendor/rand/benches/distributions/mod.rs +++ /dev/null @@ -1,3 +0,0 @@ -mod exponential; -mod normal; -mod gamma; diff --git a/vendor/rand/benches/distributions/normal.rs b/vendor/rand/benches/distributions/normal.rs deleted file mode 100644 index 1c858b1..0000000 --- a/vendor/rand/benches/distributions/normal.rs +++ /dev/null @@ -1,18 +0,0 @@ -use std::mem::size_of; -use test::Bencher; -use rand; -use rand::distributions::Sample; -use rand::distributions::normal::Normal; - -#[bench] -fn rand_normal(b: &mut Bencher) { - let mut rng = rand::weak_rng(); - let mut normal = Normal::new(-2.71828, 3.14159); - - b.iter(|| { - for _ in 0..::RAND_BENCH_N { - normal.sample(&mut rng); - } - }); - b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N; -} diff --git a/vendor/rand/benches/generators.rs b/vendor/rand/benches/generators.rs deleted file mode 100644 index daee7c5..0000000 --- a/vendor/rand/benches/generators.rs +++ /dev/null @@ -1,133 +0,0 @@ -#![feature(test)] - -extern crate test; -extern crate rand; - -const RAND_BENCH_N: u64 = 1000; -const BYTES_LEN: usize = 1024; - -use std::mem::size_of; -use test::{black_box, Bencher}; - -use rand::{Rng, StdRng, OsRng, JitterRng}; -use rand::{XorShiftRng, IsaacRng, Isaac64Rng, ChaChaRng}; - -macro_rules! gen_bytes { - ($fnn:ident, $gen:ident) => { - #[bench] - fn $fnn(b: &mut Bencher) { - let mut rng: $gen = OsRng::new().unwrap().gen(); - let mut buf = [0u8; BYTES_LEN]; - b.iter(|| { - for _ in 0..RAND_BENCH_N { - rng.fill_bytes(&mut buf); - black_box(buf); - } - }); - b.bytes = BYTES_LEN as u64 * RAND_BENCH_N; - } - } -} - -macro_rules! gen_bytes_new { - ($fnn:ident, $gen:ident) => { - #[bench] - fn $fnn(b: &mut Bencher) { - let mut rng = $gen::new().unwrap(); - let mut buf = [0u8; BYTES_LEN]; - b.iter(|| { - for _ in 0..RAND_BENCH_N { - rng.fill_bytes(&mut buf); - black_box(buf); - } - }); - b.bytes = BYTES_LEN as u64 * RAND_BENCH_N; - } - } -} - -gen_bytes!(gen_bytes_xorshift, XorShiftRng); -gen_bytes!(gen_bytes_isaac, IsaacRng); -gen_bytes!(gen_bytes_isaac64, Isaac64Rng); -gen_bytes!(gen_bytes_chacha, ChaChaRng); -gen_bytes_new!(gen_bytes_std, StdRng); -gen_bytes_new!(gen_bytes_os, OsRng); - - -macro_rules! gen_uint { - ($fnn:ident, $ty:ty, $gen:ident) => { - #[bench] - fn $fnn(b: &mut Bencher) { - let mut rng: $gen = OsRng::new().unwrap().gen(); - b.iter(|| { - for _ in 0..RAND_BENCH_N { - black_box(rng.gen::<$ty>()); - } - }); - b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N; - } - } -} - -macro_rules! gen_uint_new { - ($fnn:ident, $ty:ty, $gen:ident) => { - #[bench] - fn $fnn(b: &mut Bencher) { - let mut rng = $gen::new().unwrap(); - b.iter(|| { - for _ in 0..RAND_BENCH_N { - black_box(rng.gen::<$ty>()); - } - }); - b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N; - } - } -} - -gen_uint!(gen_u32_xorshift, u32, XorShiftRng); -gen_uint!(gen_u32_isaac, u32, IsaacRng); -gen_uint!(gen_u32_isaac64, u32, Isaac64Rng); -gen_uint!(gen_u32_chacha, u32, ChaChaRng); -gen_uint_new!(gen_u32_std, u32, StdRng); -gen_uint_new!(gen_u32_os, u32, OsRng); - -gen_uint!(gen_u64_xorshift, u64, XorShiftRng); -gen_uint!(gen_u64_isaac, u64, IsaacRng); -gen_uint!(gen_u64_isaac64, u64, Isaac64Rng); -gen_uint!(gen_u64_chacha, u64, ChaChaRng); -gen_uint_new!(gen_u64_std, u64, StdRng); -gen_uint_new!(gen_u64_os, u64, OsRng); - -#[bench] -fn gen_u64_jitter(b: &mut Bencher) { - let mut rng = JitterRng::new().unwrap(); - b.iter(|| { - black_box(rng.gen::<u64>()); - }); - b.bytes = size_of::<u64>() as u64; -} - -macro_rules! init_gen { - ($fnn:ident, $gen:ident) => { - #[bench] - fn $fnn(b: &mut Bencher) { - let mut rng: XorShiftRng = OsRng::new().unwrap().gen(); - b.iter(|| { - let r2: $gen = rng.gen(); - black_box(r2); - }); - } - } -} - -init_gen!(init_xorshift, XorShiftRng); -init_gen!(init_isaac, IsaacRng); -init_gen!(init_isaac64, Isaac64Rng); -init_gen!(init_chacha, ChaChaRng); - -#[bench] -fn init_jitter(b: &mut Bencher) { - b.iter(|| { - black_box(JitterRng::new().unwrap()); - }); -} diff --git a/vendor/rand/benches/misc.rs b/vendor/rand/benches/misc.rs deleted file mode 100644 index 4251761..0000000 --- a/vendor/rand/benches/misc.rs +++ /dev/null @@ -1,62 +0,0 @@ -#![feature(test)] - -extern crate test; -extern crate rand; - -use test::{black_box, Bencher}; - -use rand::{Rng, weak_rng}; -use rand::seq::*; - -#[bench] -fn misc_shuffle_100(b: &mut Bencher) { - let mut rng = weak_rng(); - let x : &mut [usize] = &mut [1; 100]; - b.iter(|| { - rng.shuffle(x); - black_box(&x); - }) -} - -#[bench] -fn misc_sample_iter_10_of_100(b: &mut Bencher) { - let mut rng = weak_rng(); - let x : &[usize] = &[1; 100]; - b.iter(|| { - black_box(sample_iter(&mut rng, x, 10).unwrap_or_else(|e| e)); - }) -} - -#[bench] -fn misc_sample_slice_10_of_100(b: &mut Bencher) { - let mut rng = weak_rng(); - let x : &[usize] = &[1; 100]; - b.iter(|| { - black_box(sample_slice(&mut rng, x, 10)); - }) -} - -#[bench] -fn misc_sample_slice_ref_10_of_100(b: &mut Bencher) { - let mut rng = weak_rng(); - let x : &[usize] = &[1; 100]; - b.iter(|| { - black_box(sample_slice_ref(&mut rng, x, 10)); - }) -} - -macro_rules! sample_indices { - ($name:ident, $amount:expr, $length:expr) => { - #[bench] - fn $name(b: &mut Bencher) { - let mut rng = weak_rng(); - b.iter(|| { - black_box(sample_indices(&mut rng, $length, $amount)); - }) - } - } -} - -sample_indices!(misc_sample_indices_10_of_1k, 10, 1000); -sample_indices!(misc_sample_indices_50_of_1k, 50, 1000); -sample_indices!(misc_sample_indices_100_of_1k, 100, 1000); diff --git a/vendor/rand/src/distributions/exponential.rs b/vendor/rand/src/distributions/exponential.rs deleted file mode 100644 index c3c924c..0000000 --- a/vendor/rand/src/distributions/exponential.rs +++ /dev/null @@ -1,124 +0,0 @@ -// 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. - -//! The exponential distribution. - -use {Rng, Rand}; -use distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample}; - -/// A wrapper around an `f64` to generate Exp(1) random numbers. -/// -/// See `Exp` for the general exponential distribution. -/// -/// Implemented via the ZIGNOR variant[1] of the Ziggurat method. The -/// exact description in the paper was adjusted to use tables for the -/// exponential distribution rather than normal. -/// -/// [1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random -/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield -/// College, Oxford -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::exponential::Exp1; -/// -/// let Exp1(x) = rand::random(); -/// println!("{}", x); -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct Exp1(pub f64); - -// This could be done via `-rng.gen::<f64>().ln()` but that is slower. -impl Rand for Exp1 { - #[inline] - fn rand<R:Rng>(rng: &mut R) -> Exp1 { - #[inline] - fn pdf(x: f64) -> f64 { - (-x).exp() - } - #[inline] - fn zero_case<R:Rng>(rng: &mut R, _u: f64) -> f64 { - ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln() - } - - Exp1(ziggurat(rng, false, - &ziggurat_tables::ZIG_EXP_X, - &ziggurat_tables::ZIG_EXP_F, - pdf, zero_case)) - } -} - -/// The exponential distribution `Exp(lambda)`. -/// -/// This distribution has density function: `f(x) = lambda * -/// exp(-lambda * x)` for `x > 0`. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{Exp, IndependentSample}; -/// -/// let exp = Exp::new(2.0); -/// let v = exp.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from a Exp(2) distribution", v); -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct Exp { - /// `lambda` stored as `1/lambda`, since this is what we scale by. - lambda_inverse: f64 -} - -impl Exp { - /// Construct a new `Exp` with the given shape parameter - /// `lambda`. Panics if `lambda <= 0`. - #[inline] - pub fn new(lambda: f64) -> Exp { - assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0"); - Exp { lambda_inverse: 1.0 / lambda } - } -} - -impl Sample<f64> for Exp { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for Exp { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let Exp1(n) = rng.gen::<Exp1>(); - n * self.lambda_inverse - } -} - -#[cfg(test)] -mod test { - use distributions::{Sample, IndependentSample}; - use super::Exp; - - #[test] - fn test_exp() { - let mut exp = Exp::new(10.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - assert!(exp.sample(&mut rng) >= 0.0); - assert!(exp.ind_sample(&mut rng) >= 0.0); - } - } - #[test] - #[should_panic] - fn test_exp_invalid_lambda_zero() { - Exp::new(0.0); - } - #[test] - #[should_panic] - fn test_exp_invalid_lambda_neg() { - Exp::new(-10.0); - } -} diff --git a/vendor/rand/src/distributions/gamma.rs b/vendor/rand/src/distributions/gamma.rs deleted file mode 100644 index 2806495..0000000 --- a/vendor/rand/src/distributions/gamma.rs +++ /dev/null @@ -1,386 +0,0 @@ -// 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. -// -// ignore-lexer-test FIXME #15679 - -//! The Gamma and derived distributions. - -use self::GammaRepr::*; -use self::ChiSquaredRepr::*; - -use {Rng, Open01}; -use super::normal::StandardNormal; -use super::{IndependentSample, Sample, Exp}; - -/// The Gamma distribution `Gamma(shape, scale)` distribution. -/// -/// The density function of this distribution is -/// -/// ```text -/// f(x) = x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k) -/// ``` -/// -/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the -/// scale and both `k` and `θ` are strictly positive. -/// -/// The algorithm used is that described by Marsaglia & Tsang 2000[1], -/// falling back to directly sampling from an Exponential for `shape -/// == 1`, and using the boosting technique described in [1] for -/// `shape < 1`. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{IndependentSample, Gamma}; -/// -/// let gamma = Gamma::new(2.0, 5.0); -/// let v = gamma.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from a Gamma(2, 5) distribution", v); -/// ``` -/// -/// [1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method -/// for Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3 -/// (September 2000), -/// 363-372. DOI:[10.1145/358407.358414](http://doi.acm.org/10.1145/358407.358414) -#[derive(Clone, Copy, Debug)] -pub struct Gamma { - repr: GammaRepr, -} - -#[derive(Clone, Copy, Debug)] -enum GammaRepr { - Large(GammaLargeShape), - One(Exp), - Small(GammaSmallShape) -} - -// These two helpers could be made public, but saving the -// match-on-Gamma-enum branch from using them directly (e.g. if one -// knows that the shape is always > 1) doesn't appear to be much -// faster. - -/// Gamma distribution where the shape parameter is less than 1. -/// -/// Note, samples from this require a compulsory floating-point `pow` -/// call, which makes it significantly slower than sampling from a -/// gamma distribution where the shape parameter is greater than or -/// equal to 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -#[derive(Clone, Copy, Debug)] -struct GammaSmallShape { - inv_shape: f64, - large_shape: GammaLargeShape -} - -/// Gamma distribution where the shape parameter is larger than 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -#[derive(Clone, Copy, Debug)] -struct GammaLargeShape { - scale: f64, - c: f64, - d: f64 -} - -impl Gamma { - /// Construct an object representing the `Gamma(shape, scale)` - /// distribution. - /// - /// Panics if `shape <= 0` or `scale <= 0`. - #[inline] - pub fn new(shape: f64, scale: f64) -> Gamma { - assert!(shape > 0.0, "Gamma::new called with shape <= 0"); - assert!(scale > 0.0, "Gamma::new called with scale <= 0"); - - let repr = if shape == 1.0 { - One(Exp::new(1.0 / scale)) - } else if shape < 1.0 { - Small(GammaSmallShape::new_raw(shape, scale)) - } else { - Large(GammaLargeShape::new_raw(shape, scale)) - }; - Gamma { repr: repr } - } -} - -impl GammaSmallShape { - fn new_raw(shape: f64, scale: f64) -> GammaSmallShape { - GammaSmallShape { - inv_shape: 1. / shape, - large_shape: GammaLargeShape::new_raw(shape + 1.0, scale) - } - } -} - -impl GammaLargeShape { - fn new_raw(shape: f64, scale: f64) -> GammaLargeShape { - let d = shape - 1. / 3.; - GammaLargeShape { - scale: scale, - c: 1. / (9. * d).sqrt(), - d: d - } - } -} - -impl Sample<f64> for Gamma { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl Sample<f64> for GammaSmallShape { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl Sample<f64> for GammaLargeShape { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} - -impl IndependentSample<f64> for Gamma { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - match self.repr { - Small(ref g) => g.ind_sample(rng), - One(ref g) => g.ind_sample(rng), - Large(ref g) => g.ind_sample(rng), - } - } -} -impl IndependentSample<f64> for GammaSmallShape { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let Open01(u) = rng.gen::<Open01<f64>>(); - - self.large_shape.ind_sample(rng) * u.powf(self.inv_shape) - } -} -impl IndependentSample<f64> for GammaLargeShape { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - loop { - let StandardNormal(x) = rng.gen::<StandardNormal>(); - let v_cbrt = 1.0 + self.c * x; - if v_cbrt <= 0.0 { // a^3 <= 0 iff a <= 0 - continue - } - - let v = v_cbrt * v_cbrt * v_cbrt; - let Open01(u) = rng.gen::<Open01<f64>>(); - - let x_sqr = x * x; - if u < 1.0 - 0.0331 * x_sqr * x_sqr || - u.ln() < 0.5 * x_sqr + self.d * (1.0 - v + v.ln()) { - return self.d * v * self.scale - } - } - } -} - -/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of -/// freedom. -/// -/// For `k > 0` integral, this distribution is the sum of the squares -/// of `k` independent standard normal random variables. For other -/// `k`, this uses the equivalent characterisation -/// `χ²(k) = Gamma(k/2, 2)`. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{ChiSquared, IndependentSample}; -/// -/// let chi = ChiSquared::new(11.0); -/// let v = chi.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from a χ²(11) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct ChiSquared { - repr: ChiSquaredRepr, -} - -#[derive(Clone, Copy, Debug)] -enum ChiSquaredRepr { - // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1, - // e.g. when alpha = 1/2 as it would be for this case, so special- - // casing and using the definition of N(0,1)^2 is faster. - DoFExactlyOne, - DoFAnythingElse(Gamma), -} - -impl ChiSquared { - /// Create a new chi-squared distribution with degrees-of-freedom - /// `k`. Panics if `k < 0`. - pub fn new(k: f64) -> ChiSquared { - let repr = if k == 1.0 { - DoFExactlyOne - } else { - assert!(k > 0.0, "ChiSquared::new called with `k` < 0"); - DoFAnythingElse(Gamma::new(0.5 * k, 2.0)) - }; - ChiSquared { repr: repr } - } -} -impl Sample<f64> for ChiSquared { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for ChiSquared { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - match self.repr { - DoFExactlyOne => { - // k == 1 => N(0,1)^2 - let StandardNormal(norm) = rng.gen::<StandardNormal>(); - norm * norm - } - DoFAnythingElse(ref g) => g.ind_sample(rng) - } - } -} - -/// The Fisher F distribution `F(m, n)`. -/// -/// This distribution is equivalent to the ratio of two normalised -/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) / -/// (χ²(n)/n)`. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{FisherF, IndependentSample}; -/// -/// let f = FisherF::new(2.0, 32.0); -/// let v = f.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from an F(2, 32) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct FisherF { - numer: ChiSquared, - denom: ChiSquared, - // denom_dof / numer_dof so that this can just be a straight - // multiplication, rather than a division. - dof_ratio: f64, -} - -impl FisherF { - /// Create a new `FisherF` distribution, with the given - /// parameter. Panics if either `m` or `n` are not positive. - pub fn new(m: f64, n: f64) -> FisherF { - assert!(m > 0.0, "FisherF::new called with `m < 0`"); - assert!(n > 0.0, "FisherF::new called with `n < 0`"); - - FisherF { - numer: ChiSquared::new(m), - denom: ChiSquared::new(n), - dof_ratio: n / m - } - } -} -impl Sample<f64> for FisherF { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for FisherF { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - self.numer.ind_sample(rng) / self.denom.ind_sample(rng) * self.dof_ratio - } -} - -/// The Student t distribution, `t(nu)`, where `nu` is the degrees of -/// freedom. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{StudentT, IndependentSample}; -/// -/// let t = StudentT::new(11.0); -/// let v = t.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from a t(11) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct StudentT { - chi: ChiSquared, - dof: f64 -} - -impl StudentT { - /// Create a new Student t distribution with `n` degrees of - /// freedom. Panics if `n <= 0`. - pub fn new(n: f64) -> StudentT { - assert!(n > 0.0, "StudentT::new called with `n <= 0`"); - StudentT { - chi: ChiSquared::new(n), - dof: n - } - } -} -impl Sample<f64> for StudentT { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for StudentT { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let StandardNormal(norm) = rng.gen::<StandardNormal>(); - norm * (self.dof / self.chi.ind_sample(rng)).sqrt() - } -} - -#[cfg(test)] -mod test { - use distributions::{Sample, IndependentSample}; - use super::{ChiSquared, StudentT, FisherF}; - - #[test] - fn test_chi_squared_one() { - let mut chi = ChiSquared::new(1.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - fn test_chi_squared_small() { - let mut chi = ChiSquared::new(0.5); - let mut rng = ::test::rng(); - for _ in 0..1000 { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - fn test_chi_squared_large() { - let mut chi = ChiSquared::new(30.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_chi_squared_invalid_dof() { - ChiSquared::new(-1.0); - } - - #[test] - fn test_f() { - let mut f = FisherF::new(2.0, 32.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - f.sample(&mut rng); - f.ind_sample(&mut rng); - } - } - - #[test] - fn test_t() { - let mut t = StudentT::new(11.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - t.sample(&mut rng); - t.ind_sample(&mut rng); - } - } -} diff --git a/vendor/rand/src/distributions/mod.rs b/vendor/rand/src/distributions/mod.rs deleted file mode 100644 index 5de8efb..0000000 --- a/vendor/rand/src/distributions/mod.rs +++ /dev/null @@ -1,409 +0,0 @@ -// 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 }]); - } -} diff --git a/vendor/rand/src/distributions/normal.rs b/vendor/rand/src/distributions/normal.rs deleted file mode 100644 index 280613d..0000000 --- a/vendor/rand/src/distributions/normal.rs +++ /dev/null @@ -1,201 +0,0 @@ -// 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. - -//! The normal and derived distributions. - -use {Rng, Rand, Open01}; -use distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample}; - -/// A wrapper around an `f64` to generate N(0, 1) random numbers -/// (a.k.a. a standard normal, or Gaussian). -/// -/// See `Normal` for the general normal distribution. -/// -/// Implemented via the ZIGNOR variant[1] of the Ziggurat method. -/// -/// [1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random -/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield -/// College, Oxford -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::normal::StandardNormal; -/// -/// let StandardNormal(x) = rand::random(); -/// println!("{}", x); -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct StandardNormal(pub f64); - -impl Rand for StandardNormal { - fn rand<R:Rng>(rng: &mut R) -> StandardNormal { - #[inline] - fn pdf(x: f64) -> f64 { - (-x*x/2.0).exp() - } - #[inline] - fn zero_case<R:Rng>(rng: &mut R, u: f64) -> f64 { - // compute a random number in the tail by hand - - // strange initial conditions, because the loop is not - // do-while, so the condition should be true on the first - // run, they get overwritten anyway (0 < 1, so these are - // good). - let mut x = 1.0f64; - let mut y = 0.0f64; - - while -2.0 * y < x * x { - let Open01(x_) = rng.gen::<Open01<f64>>(); - let Open01(y_) = rng.gen::<Open01<f64>>(); - - x = x_.ln() / ziggurat_tables::ZIG_NORM_R; - y = y_.ln(); - } - - if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x } - } - - StandardNormal(ziggurat( - rng, - true, // this is symmetric - &ziggurat_tables::ZIG_NORM_X, - &ziggurat_tables::ZIG_NORM_F, - pdf, zero_case)) - } -} - -/// The normal distribution `N(mean, std_dev**2)`. -/// -/// This uses the ZIGNOR variant of the Ziggurat method, see -/// `StandardNormal` for more details. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{Normal, IndependentSample}; -/// -/// // mean 2, standard deviation 3 -/// let normal = Normal::new(2.0, 3.0); -/// let v = normal.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from a N(2, 9) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct Normal { - mean: f64, - std_dev: f64, -} - -impl Normal { - /// Construct a new `Normal` distribution with the given mean and - /// standard deviation. - /// - /// # Panics - /// - /// Panics if `std_dev < 0`. - #[inline] - pub fn new(mean: f64, std_dev: f64) -> Normal { - assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0"); - Normal { - mean: mean, - std_dev: std_dev - } - } -} -impl Sample<f64> for Normal { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for Normal { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let StandardNormal(n) = rng.gen::<StandardNormal>(); - self.mean + self.std_dev * n - } -} - - -/// The log-normal distribution `ln N(mean, std_dev**2)`. -/// -/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, -/// std_dev**2)` distributed. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{LogNormal, IndependentSample}; -/// -/// // mean 2, standard deviation 3 -/// let log_normal = LogNormal::new(2.0, 3.0); -/// let v = log_normal.ind_sample(&mut rand::thread_rng()); -/// println!("{} is from an ln N(2, 9) distribution", v) -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct LogNormal { - norm: Normal -} - -impl LogNormal { - /// Construct a new `LogNormal` distribution with the given mean - /// and standard deviation. - /// - /// # Panics - /// - /// Panics if `std_dev < 0`. - #[inline] - pub fn new(mean: f64, std_dev: f64) -> LogNormal { - assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0"); - LogNormal { norm: Normal::new(mean, std_dev) } - } -} -impl Sample<f64> for LogNormal { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for LogNormal { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - self.norm.ind_sample(rng).exp() - } -} - -#[cfg(test)] -mod tests { - use distributions::{Sample, IndependentSample}; - use super::{Normal, LogNormal}; - - #[test] - fn test_normal() { - let mut norm = Normal::new(10.0, 10.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - norm.sample(&mut rng); - norm.ind_sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_normal_invalid_sd() { - Normal::new(10.0, -1.0); - } - - - #[test] - fn test_log_normal() { - let mut lnorm = LogNormal::new(10.0, 10.0); - let mut rng = ::test::rng(); - for _ in 0..1000 { - lnorm.sample(&mut rng); - lnorm.ind_sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_log_normal_invalid_sd() { - LogNormal::new(10.0, -1.0); - } -} diff --git a/vendor/rand/src/distributions/range.rs b/vendor/rand/src/distributions/range.rs deleted file mode 100644 index 935a00a..0000000 --- a/vendor/rand/src/distributions/range.rs +++ /dev/null @@ -1,241 +0,0 @@ -// 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. - -//! Generating numbers between two others. - -// this is surprisingly complicated to be both generic & correct - -use core::num::Wrapping as w; - -use Rng; -use distributions::{Sample, IndependentSample}; - -/// Sample values uniformly between two bounds. -/// -/// This gives a uniform distribution (assuming the RNG used to sample -/// it is itself uniform & the `SampleRange` implementation for the -/// given type is correct), even for edge cases like `low = 0u8`, -/// `high = 170u8`, for which a naive modulo operation would return -/// numbers less than 85 with double the probability to those greater -/// than 85. -/// -/// Types should attempt to sample in `[low, high)`, i.e., not -/// including `high`, but this may be very difficult. All the -/// primitive integer types satisfy this property, and the float types -/// normally satisfy it, but rounding may mean `high` can occur. -/// -/// # Example -/// -/// ```rust -/// use rand::distributions::{IndependentSample, Range}; -/// -/// fn main() { -/// let between = Range::new(10, 10000); -/// let mut rng = rand::thread_rng(); -/// let mut sum = 0; -/// for _ in 0..1000 { -/// sum += between.ind_sample(&mut rng); -/// } -/// println!("{}", sum); -/// } -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct Range<X> { - low: X, - range: X, - accept_zone: X -} - -impl<X: SampleRange + PartialOrd> Range<X> { - /// Create a new `Range` instance that samples uniformly from - /// `[low, high)`. Panics if `low >= high`. - pub fn new(low: X, high: X) -> Range<X> { - assert!(low < high, "Range::new called with `low >= high`"); - SampleRange::construct_range(low, high) - } -} - -impl<Sup: SampleRange> Sample<Sup> for Range<Sup> { - #[inline] - fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) } -} -impl<Sup: SampleRange> IndependentSample<Sup> for Range<Sup> { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup { - SampleRange::sample_range(self, rng) - } -} - -/// The helper trait for types that have a sensible way to sample -/// uniformly between two values. This should not be used directly, -/// and is only to facilitate `Range`. -pub trait SampleRange : Sized { - /// Construct the `Range` object that `sample_range` - /// requires. This should not ever be called directly, only via - /// `Range::new`, which will check that `low < high`, so this - /// function doesn't have to repeat the check. - fn construct_range(low: Self, high: Self) -> Range<Self>; - - /// Sample a value from the given `Range` with the given `Rng` as - /// a source of randomness. - fn sample_range<R: Rng>(r: &Range<Self>, rng: &mut R) -> Self; -} - -macro_rules! integer_impl { - ($ty:ty, $unsigned:ident) => { - impl SampleRange for $ty { - // we play free and fast with unsigned vs signed here - // (when $ty is signed), but that's fine, since the - // contract of this macro is for $ty and $unsigned to be - // "bit-equal", so casting between them is a no-op & a - // bijection. - - #[inline] - fn construct_range(low: $ty, high: $ty) -> Range<$ty> { - let range = (w(high as $unsigned) - w(low as $unsigned)).0; - let unsigned_max: $unsigned = ::core::$unsigned::MAX; - - // this is the largest number that fits into $unsigned - // that `range` divides evenly, so, if we've sampled - // `n` uniformly from this region, then `n % range` is - // uniform in [0, range) - let zone = unsigned_max - unsigned_max % range; - - Range { - low: low, - range: range as $ty, - accept_zone: zone as $ty - } - } - - #[inline] - fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { - loop { - // rejection sample - let v = rng.gen::<$unsigned>(); - // until we find something that fits into the - // region which r.range evenly divides (this will - // be uniformly distributed) - if v < r.accept_zone as $unsigned { - // and return it, with some adjustments - return (w(r.low) + w((v % r.range as $unsigned) as $ty)).0; - } - } - } - } - } -} - -integer_impl! { i8, u8 } -integer_impl! { i16, u16 } -integer_impl! { i32, u32 } -integer_impl! { i64, u64 } -#[cfg(feature = "i128_support")] -integer_impl! { i128, u128 } -integer_impl! { isize, usize } -integer_impl! { u8, u8 } -integer_impl! { u16, u16 } -integer_impl! { u32, u32 } -integer_impl! { u64, u64 } -#[cfg(feature = "i128_support")] -integer_impl! { u128, u128 } -integer_impl! { usize, usize } - -macro_rules! float_impl { - ($ty:ty) => { - impl SampleRange for $ty { - fn construct_range(low: $ty, high: $ty) -> Range<$ty> { - Range { - low: low, - range: high - low, - accept_zone: 0.0 // unused - } - } - fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { - r.low + r.range * rng.gen::<$ty>() - } - } - } -} - -float_impl! { f32 } -float_impl! { f64 } - -#[cfg(test)] -mod tests { - use distributions::{Sample, IndependentSample}; - use super::Range as Range; - - #[should_panic] - #[test] - fn test_range_bad_limits_equal() { - Range::new(10, 10); - } - #[should_panic] - #[test] - fn test_range_bad_limits_flipped() { - Range::new(10, 5); - } - - #[test] - fn test_integers() { - let mut rng = ::test::rng(); - macro_rules! t { - ($($ty:ident),*) => {{ - $( - let v: &[($ty, $ty)] = &[(0, 10), - (10, 127), - (::core::$ty::MIN, ::core::$ty::MAX)]; - for &(low, high) in v.iter() { - let mut sampler: Range<$ty> = Range::new(low, high); - for _ in 0..1000 { - let v = sampler.sample(&mut rng); - assert!(low <= v && v < high); - let v = sampler.ind_sample(&mut rng); - assert!(low <= v && v < high); - } - } - )* - }} - } - #[cfg(not(feature = "i128_support"))] - t!(i8, i16, i32, i64, isize, - u8, u16, u32, u64, usize); - #[cfg(feature = "i128_support")] - t!(i8, i16, i32, i64, i128, isize, - u8, u16, u32, u64, u128, usize); - } - - #[test] - fn test_floats() { - let mut rng = ::test::rng(); - macro_rules! t { - ($($ty:ty),*) => {{ - $( - let v: &[($ty, $ty)] = &[(0.0, 100.0), - (-1e35, -1e25), - (1e-35, 1e-25), - (-1e35, 1e35)]; - for &(low, high) in v.iter() { - let mut sampler: Range<$ty> = Range::new(low, high); - for _ in 0..1000 { - let v = sampler.sample(&mut rng); - assert!(low <= v && v < high); - let v = sampler.ind_sample(&mut rng); - assert!(low <= v && v < high); - } - } - )* - }} - } - - t!(f32, f64) - } - -} diff --git a/vendor/rand/src/distributions/ziggurat_tables.rs b/vendor/rand/src/distributions/ziggurat_tables.rs deleted file mode 100644 index b6de4bf..0000000 --- a/vendor/rand/src/distributions/ziggurat_tables.rs +++ /dev/null @@ -1,280 +0,0 @@ -// 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. - -// Tables for distributions which are sampled using the ziggurat -// algorithm. Autogenerated by `ziggurat_tables.py`. - -pub type ZigTable = &'static [f64; 257]; -pub const ZIG_NORM_R: f64 = 3.654152885361008796; -pub static ZIG_NORM_X: [f64; 257] = - [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074, - 3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434, - 2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548, - 2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056, - 2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570, - 2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761, - 2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318, - 2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520, - 2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952, - 2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565, - 2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760, - 2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995, - 2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268, - 2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957, - 2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778, - 2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715, - 2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244, - 1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896, - 1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257, - 1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081, - 1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281, - 1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566, - 1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199, - 1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933, - 1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012, - 1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086, - 1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338, - 1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526, - 1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427, - 1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339, - 1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456, - 1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553, - 1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404, - 1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369, - 1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830, - 1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425, - 1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534, - 1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964, - 1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606, - 1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679, - 1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728, - 1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732, - 1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903, - 1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552, - 1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650, - 1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240, - 1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975, - 1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151, - 1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714, - 1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538, - 1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441, - 1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750, - 0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130, - 0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997, - 0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550, - 0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752, - 0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785, - 0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653, - 0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448, - 0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928, - 0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262, - 0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393, - 0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746, - 0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806, - 0.000000000000000000]; -pub static ZIG_NORM_F: [f64; 257] = - [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872, - 0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100, - 0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839, - 0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237, - 0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690, - 0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918, - 0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664, - 0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916, - 0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854, - 0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965, - 0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509, - 0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229, - 0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627, - 0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880, - 0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014, - 0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349, - 0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352, - 0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926, - 0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563, - 0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071, - 0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654, - 0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926, - 0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112, - 0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651, - 0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589, - 0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525, - 0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988, - 0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150, - 0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837, - 0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316, - 0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984, - 0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274, - 0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396, - 0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099, - 0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340, - 0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515, - 0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344, - 0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958, - 0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668, - 0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784, - 0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519, - 0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750, - 0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481, - 0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788, - 0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658, - 0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142, - 0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700, - 0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941, - 0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916, - 0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473, - 0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719, - 0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205, - 0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991, - 0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357, - 0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376, - 0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409, - 0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437, - 0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500, - 0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902, - 0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935, - 0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077, - 0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839, - 0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247, - 0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328, - 1.000000000000000000]; -pub const ZIG_EXP_R: f64 = 7.697117470131050077; -pub static ZIG_EXP_X: [f64; 257] = - [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696, - 6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488, - 5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530, - 4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380, - 4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857, - 4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762, - 3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744, - 3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770, - 3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608, - 3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405, - 3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160, - 3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481, - 3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601, - 2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825, - 2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780, - 2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752, - 2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489, - 2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970, - 2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815, - 2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886, - 2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372, - 2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213, - 2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027, - 2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289, - 2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526, - 2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563, - 1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943, - 1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242, - 1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954, - 1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014, - 1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566, - 1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896, - 1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334, - 1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892, - 1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092, - 1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058, - 1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504, - 1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137, - 1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189, - 1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117, - 1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330, - 1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124, - 1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677, - 1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511, - 1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813, - 1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209, - 1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735, - 0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509, - 0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311, - 0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066, - 0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206, - 0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430, - 0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102, - 0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959, - 0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947, - 0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030, - 0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626, - 0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398, - 0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235, - 0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765, - 0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122, - 0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703, - 0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842, - 0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570, - 0.000000000000000000]; -pub static ZIG_EXP_F: [f64; 257] = - [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573, - 0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797, - 0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991, - 0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981, - 0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943, - 0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355, - 0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581, - 0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221, - 0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622, - 0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431, - 0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139, - 0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289, - 0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379, - 0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030, - 0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660, - 0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816, - 0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752, - 0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435, - 0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146, - 0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197, - 0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213, - 0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145, - 0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283, - 0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641, - 0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671, - 0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602, - 0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146, - 0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839, - 0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129, - 0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081, - 0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829, - 0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083, - 0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189, - 0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654, - 0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628, - 0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956, - 0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560, - 0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543, - 0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173, - 0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967, - 0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746, - 0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252, - 0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185, - 0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223, - 0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717, - 0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449, - 0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379, - 0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056, - 0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350, - 0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209, - 0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907, - 0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836, - 0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708, - 0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881, - 0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931, - 0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056, - 0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150, - 0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560, - 0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398, - 0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177, - 0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456, - 0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838, - 0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101, - 0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477, - 1.000000000000000000]; diff --git a/vendor/rand/src/jitter.rs b/vendor/rand/src/jitter.rs deleted file mode 100644 index 3693481..0000000 --- a/vendor/rand/src/jitter.rs +++ /dev/null @@ -1,754 +0,0 @@ -// Copyright 2017 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. -// -// Based on jitterentropy-library, http://www.chronox.de/jent.html. -// Copyright Stephan Mueller <smueller@chronox.de>, 2014 - 2017. -// -// With permission from Stephan Mueller to relicense the Rust translation under -// the MIT license. - -//! Non-physical true random number generator based on timing jitter. - -use Rng; - -use core::{fmt, mem, ptr}; -#[cfg(feature="std")] -use std::sync::atomic::{AtomicUsize, ATOMIC_USIZE_INIT, Ordering}; - -const MEMORY_BLOCKS: usize = 64; -const MEMORY_BLOCKSIZE: usize = 32; -const MEMORY_SIZE: usize = MEMORY_BLOCKS * MEMORY_BLOCKSIZE; - -/// A true random number generator based on jitter in the CPU execution time, -/// and jitter in memory access time. -/// -/// This is a true random number generator, as opposed to pseudo-random -/// generators. Random numbers generated by `JitterRng` can be seen as fresh -/// entropy. A consequence is that is orders of magnitude slower than `OsRng` -/// and PRNGs (about 10^3 .. 10^6 slower). -/// -/// There are very few situations where using this RNG is appropriate. Only very -/// few applications require true entropy. A normal PRNG can be statistically -/// indistinguishable, and a cryptographic PRNG should also be as impossible to -/// predict. -/// -/// Use of `JitterRng` is recommended for initializing cryptographic PRNGs when -/// `OsRng` is not available. -/// -/// This implementation is based on -/// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0. -// -// Note: the C implementation relies on being compiled without optimizations. -// This implementation goes through lengths to make the compiler not optimise -// out what is technically dead code, but that does influence timing jitter. -pub struct JitterRng { - data: u64, // Actual random number - // Number of rounds to run the entropy collector per 64 bits - rounds: u32, - // Timer and previous time stamp, used by `measure_jitter` - timer: fn() -> u64, - prev_time: u64, - // Deltas used for the stuck test - last_delta: i64, - last_delta2: i64, - // Memory for the Memory Access noise source - mem_prev_index: usize, - mem: [u8; MEMORY_SIZE], - // Make `next_u32` not waste 32 bits - data_remaining: Option<u32>, -} - -// Custom Debug implementation that does not expose the internal state -impl fmt::Debug for JitterRng { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "JitterRng {{}}") - } -} - -/// An error that can occur when `test_timer` fails. -#[derive(Debug, Clone, PartialEq, Eq)] -pub enum TimerError { - /// No timer available. - NoTimer, - /// Timer too coarse to use as an entropy source. - CoarseTimer, - /// Timer is not monotonically increasing. - NotMonotonic, - /// Variations of deltas of time too small. - TinyVariantions, - /// Too many stuck results (indicating no added entropy). - TooManyStuck, - #[doc(hidden)] - __Nonexhaustive, -} - -impl TimerError { - fn description(&self) -> &'static str { - match *self { - TimerError::NoTimer => "no timer available", - TimerError::CoarseTimer => "coarse timer", - TimerError::NotMonotonic => "timer not monotonic", - TimerError::TinyVariantions => "time delta variations too small", - TimerError::TooManyStuck => "too many stuck results", - TimerError::__Nonexhaustive => unreachable!(), - } - } -} - -impl fmt::Display for TimerError { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "{}", self.description()) - } -} - -#[cfg(feature="std")] -impl ::std::error::Error for TimerError { - fn description(&self) -> &str { - self.description() - } -} - -// Initialise to zero; must be positive -#[cfg(feature="std")] -static JITTER_ROUNDS: AtomicUsize = ATOMIC_USIZE_INIT; - -impl JitterRng { - /// Create a new `JitterRng`. - /// Makes use of `std::time` for a timer. - /// - /// During initialization CPU execution timing jitter is measured a few - /// hundred times. If this does not pass basic quality tests, an error is - /// returned. The test result is cached to make subsequent calls faster. - #[cfg(feature="std")] - pub fn new() -> Result<JitterRng, TimerError> { - let mut ec = JitterRng::new_with_timer(platform::get_nstime); - let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u32; - if rounds == 0 { - // No result yet: run test. - // This allows the timer test to run multiple times; we don't care. - rounds = ec.test_timer()?; - JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed); - } - ec.set_rounds(rounds); - Ok(ec) - } - - /// Create a new `JitterRng`. - /// A custom timer can be supplied, making it possible to use `JitterRng` in - /// `no_std` environments. - /// - /// The timer must have nanosecond precision. - /// - /// This method is more low-level than `new()`. It is the responsibility of - /// the caller to run `test_timer` before using any numbers generated with - /// `JitterRng`, and optionally call `set_rounds()`. - pub fn new_with_timer(timer: fn() -> u64) -> JitterRng { - let mut ec = JitterRng { - data: 0, - rounds: 64, - timer: timer, - prev_time: 0, - last_delta: 0, - last_delta2: 0, - mem_prev_index: 0, - mem: [0; MEMORY_SIZE], - data_remaining: None, - }; - - // Fill `data`, `prev_time`, `last_delta` and `last_delta2` with - // non-zero values. - ec.prev_time = timer(); - ec.gen_entropy(); - - // Do a single read from `self.mem` to make sure the Memory Access noise - // source is not optimised out. - // Note: this read is important, it effects optimisations for the entire - // module! - black_box(ec.mem[0]); - - ec - } - - /// Configures how many rounds are used to generate each 64-bit value. - /// This must be greater than zero, and has a big impact on performance - /// and output quality. - /// - /// `new_with_timer` conservatively uses 64 rounds, but often less rounds - /// can be used. The `test_timer()` function returns the minimum number of - /// rounds required for full strength (platform dependent), so one may use - /// `rng.set_rounds(rng.test_timer()?);` or cache the value. - pub fn set_rounds(&mut self, rounds: u32) { - assert!(rounds > 0); - self.rounds = rounds; - } - - // Calculate a random loop count used for the next round of an entropy - // collection, based on bits from a fresh value from the timer. - // - // The timer is folded to produce a number that contains at most `n_bits` - // bits. - // - // Note: A constant should be added to the resulting random loop count to - // prevent loops that run 0 times. - #[inline(never)] - fn random_loop_cnt(&mut self, n_bits: u32) -> u32 { - let mut rounds = 0; - - let mut time = (self.timer)(); - // Mix with the current state of the random number balance the random - // loop counter a bit more. - time ^= self.data; - - // We fold the time value as much as possible to ensure that as many - // bits of the time stamp are included as possible. - let folds = (64 + n_bits - 1) / n_bits; - let mask = (1 << n_bits) - 1; - for _ in 0..folds { - rounds ^= time & mask; - time = time >> n_bits; - } - - rounds as u32 - } - - // CPU jitter noise source - // Noise source based on the CPU execution time jitter - // - // This function injects the individual bits of the time value into the - // entropy pool using an LFSR. - // - // The code is deliberately inefficient with respect to the bit shifting. - // This function not only acts as folding operation, but this function's - // execution is used to measure the CPU execution time jitter. Any change to - // the loop in this function implies that careful retesting must be done. - #[inline(never)] - fn lfsr_time(&mut self, time: u64, var_rounds: bool) { - fn lfsr(mut data: u64, time: u64) -> u64{ - for i in 1..65 { - let mut tmp = time << (64 - i); - tmp = tmp >> (64 - 1); - - // Fibonacci LSFR with polynomial of - // x^64 + x^61 + x^56 + x^31 + x^28 + x^23 + 1 which is - // primitive according to - // http://poincare.matf.bg.ac.rs/~ezivkovm/publications/primpol1.pdf - // (the shift values are the polynomial values minus one - // due to counting bits from 0 to 63). As the current - // position is always the LSB, the polynomial only needs - // to shift data in from the left without wrap. - data ^= tmp; - data ^= (data >> 63) & 1; - data ^= (data >> 60) & 1; - data ^= (data >> 55) & 1; - data ^= (data >> 30) & 1; - data ^= (data >> 27) & 1; - data ^= (data >> 22) & 1; - data = data.rotate_left(1); - } - data - } - - // Note: in the reference implementation only the last round effects - // `self.data`, all the other results are ignored. To make sure the - // other rounds are not optimised out, we first run all but the last - // round on a throw-away value instead of the real `self.data`. - let mut lfsr_loop_cnt = 0; - if var_rounds { lfsr_loop_cnt = self.random_loop_cnt(4) }; - - let mut throw_away: u64 = 0; - for _ in 0..lfsr_loop_cnt { - throw_away = lfsr(throw_away, time); - } - black_box(throw_away); - - self.data = lfsr(self.data, time); - } - - // Memory Access noise source - // This is a noise source based on variations in memory access times - // - // This function performs memory accesses which will add to the timing - // variations due to an unknown amount of CPU wait states that need to be - // added when accessing memory. The memory size should be larger than the L1 - // caches as outlined in the documentation and the associated testing. - // - // The L1 cache has a very high bandwidth, albeit its access rate is usually - // slower than accessing CPU registers. Therefore, L1 accesses only add - // minimal variations as the CPU has hardly to wait. Starting with L2, - // significant variations are added because L2 typically does not belong to - // the CPU any more and therefore a wider range of CPU wait states is - // necessary for accesses. L3 and real memory accesses have even a wider - // range of wait states. However, to reliably access either L3 or memory, - // the `self.mem` memory must be quite large which is usually not desirable. - #[inline(never)] - fn memaccess(&mut self, var_rounds: bool) { - let mut acc_loop_cnt = 128; - if var_rounds { acc_loop_cnt += self.random_loop_cnt(4) }; - - let mut index = self.mem_prev_index; - for _ in 0..acc_loop_cnt { - // Addition of memblocksize - 1 to index with wrap around logic to - // ensure that every memory location is hit evenly. - // The modulus also allows the compiler to remove the indexing - // bounds check. - index = (index + MEMORY_BLOCKSIZE - 1) % MEMORY_SIZE; - - // memory access: just add 1 to one byte - // memory access implies read from and write to memory location - let tmp = self.mem[index]; - self.mem[index] = tmp.wrapping_add(1); - } - self.mem_prev_index = index; - } - - - // Stuck test by checking the: - // - 1st derivation of the jitter measurement (time delta) - // - 2nd derivation of the jitter measurement (delta of time deltas) - // - 3rd derivation of the jitter measurement (delta of delta of time - // deltas) - // - // All values must always be non-zero. - // This test is a heuristic to see whether the last measurement holds - // entropy. - fn stuck(&mut self, current_delta: i64) -> bool { - let delta2 = self.last_delta - current_delta; - let delta3 = delta2 - self.last_delta2; - - self.last_delta = current_delta; - self.last_delta2 = delta2; - - current_delta == 0 || delta2 == 0 || delta3 == 0 - } - - // This is the heart of the entropy generation: calculate time deltas and - // use the CPU jitter in the time deltas. The jitter is injected into the - // entropy pool. - // - // Ensure that `self.prev_time` is primed before using the output of this - // function. This can be done by calling this function and not using its - // result. - fn measure_jitter(&mut self) -> Option<()> { - // Invoke one noise source before time measurement to add variations - self.memaccess(true); - - // Get time stamp and calculate time delta to previous - // invocation to measure the timing variations - let time = (self.timer)(); - // Note: wrapping_sub combined with a cast to `i64` generates a correct - // delta, even in the unlikely case this is a timer that is not strictly - // monotonic. - let current_delta = time.wrapping_sub(self.prev_time) as i64; - self.prev_time = time; - - // Call the next noise source which also injects the data - self.lfsr_time(current_delta as u64, true); - - // Check whether we have a stuck measurement (i.e. does the last - // measurement holds entropy?). - if self.stuck(current_delta) { return None }; - - // Rotate the data buffer by a prime number (any odd number would - // do) to ensure that every bit position of the input time stamp - // has an even chance of being merged with a bit position in the - // entropy pool. We do not use one here as the adjacent bits in - // successive time deltas may have some form of dependency. The - // chosen value of 7 implies that the low 7 bits of the next - // time delta value is concatenated with the current time delta. - self.data = self.data.rotate_left(7); - - Some(()) - } - - // Shuffle the pool a bit by mixing some value with a bijective function - // (XOR) into the pool. - // - // The function generates a mixer value that depends on the bits set and - // the location of the set bits in the random number generated by the - // entropy source. Therefore, based on the generated random number, this - // mixer value can have 2^64 different values. That mixer value is - // initialized with the first two SHA-1 constants. After obtaining the - // mixer value, it is XORed into the random number. - // - // The mixer value is not assumed to contain any entropy. But due to the - // XOR operation, it can also not destroy any entropy present in the - // entropy pool. - #[inline(never)] - fn stir_pool(&mut self) { - // This constant is derived from the first two 32 bit initialization - // vectors of SHA-1 as defined in FIPS 180-4 section 5.3.1 - // The order does not really matter as we do not rely on the specific - // numbers. We just pick the SHA-1 constants as they have a good mix of - // bit set and unset. - const CONSTANT: u64 = 0x67452301efcdab89; - - // The start value of the mixer variable is derived from the third - // and fourth 32 bit initialization vector of SHA-1 as defined in - // FIPS 180-4 section 5.3.1 - let mut mixer = 0x98badcfe10325476; - - // This is a constant time function to prevent leaking timing - // information about the random number. - // The normal code is: - // ``` - // for i in 0..64 { - // if ((self.data >> i) & 1) == 1 { mixer ^= CONSTANT; } - // } - // ``` - // This is a bit fragile, as LLVM really wants to use branches here, and - // we rely on it to not recognise the opportunity. - for i in 0..64 { - let apply = (self.data >> i) & 1; - let mask = !apply.wrapping_sub(1); - mixer ^= CONSTANT & mask; - mixer = mixer.rotate_left(1); - } - - self.data ^= mixer; - } - - fn gen_entropy(&mut self) -> u64 { - // Prime `self.prev_time`, and run the noice sources to make sure the - // first loop round collects the expected entropy. - let _ = self.measure_jitter(); - - for _ in 0..self.rounds { - // If a stuck measurement is received, repeat measurement - // Note: we do not guard against an infinite loop, that would mean - // the timer suddenly became broken. - while self.measure_jitter().is_none() {} - } - - self.stir_pool(); - self.data - } - - /// Basic quality tests on the timer, by measuring CPU timing jitter a few - /// hundred times. - /// - /// If succesful, this will return the estimated number of rounds necessary - /// to collect 64 bits of entropy. Otherwise a `TimerError` with the cause - /// of the failure will be returned. - pub fn test_timer(&mut self) -> Result<u32, TimerError> { - // We could add a check for system capabilities such as `clock_getres` - // or check for `CONFIG_X86_TSC`, but it does not make much sense as the - // following sanity checks verify that we have a high-resolution timer. - - #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] - return Err(TimerError::NoTimer); - - let mut delta_sum = 0; - let mut old_delta = 0; - - let mut time_backwards = 0; - let mut count_mod = 0; - let mut count_stuck = 0; - - // TESTLOOPCOUNT needs some loops to identify edge systems. - // 100 is definitely too little. - const TESTLOOPCOUNT: u64 = 300; - const CLEARCACHE: u64 = 100; - - for i in 0..(CLEARCACHE + TESTLOOPCOUNT) { - // Measure time delta of core entropy collection logic - let time = (self.timer)(); - self.memaccess(true); - self.lfsr_time(time, true); - let time2 = (self.timer)(); - - // Test whether timer works - if time == 0 || time2 == 0 { - return Err(TimerError::NoTimer); - } - let delta = time2.wrapping_sub(time) as i64; - - // Test whether timer is fine grained enough to provide delta even - // when called shortly after each other -- this implies that we also - // have a high resolution timer - if delta == 0 { - return Err(TimerError::CoarseTimer); - } - - // Up to here we did not modify any variable that will be - // evaluated later, but we already performed some work. Thus we - // already have had an impact on the caches, branch prediction, - // etc. with the goal to clear it to get the worst case - // measurements. - if i < CLEARCACHE { continue; } - - if self.stuck(delta) { count_stuck += 1; } - - // Test whether we have an increasing timer. - if !(time2 > time) { time_backwards += 1; } - - // Count the number of times the counter increases in steps of 100ns - // or greater. - if (delta % 100) == 0 { count_mod += 1; } - - // Ensure that we have a varying delta timer which is necessary for - // the calculation of entropy -- perform this check only after the - // first loop is executed as we need to prime the old_delta value - delta_sum += (delta - old_delta).abs() as u64; - old_delta = delta; - } - - // We allow the time to run backwards for up to three times. - // This can happen if the clock is being adjusted by NTP operations. - // If such an operation just happens to interfere with our test, it - // should not fail. The value of 3 should cover the NTP case being - // performed during our test run. - if time_backwards > 3 { - return Err(TimerError::NotMonotonic); - } - - // Test that the available amount of entropy per round does not get to - // low. We expect 1 bit of entropy per round as a reasonable minimum - // (although less is possible, it means the collector loop has to run - // much more often). - // `assert!(delta_average >= log2(1))` - // `assert!(delta_sum / TESTLOOPCOUNT >= 1)` - // `assert!(delta_sum >= TESTLOOPCOUNT)` - if delta_sum < TESTLOOPCOUNT { - return Err(TimerError::TinyVariantions); - } - - // Ensure that we have variations in the time stamp below 100 for at - // least 10% of all checks -- on some platforms, the counter increments - // in multiples of 100, but not always - if count_mod > (TESTLOOPCOUNT * 9 / 10) { - return Err(TimerError::CoarseTimer); - } - - // If we have more than 90% stuck results, then this Jitter RNG is - // likely to not work well. - if count_stuck > (TESTLOOPCOUNT * 9 / 10) { - return Err(TimerError::TooManyStuck); - } - - // Estimate the number of `measure_jitter` rounds necessary for 64 bits - // of entropy. - // - // We don't try very hard to come up with a good estimate of the - // available bits of entropy per round here for two reasons: - // 1. Simple estimates of the available bits (like Shannon entropy) are - // too optimistic. - // 2) Unless we want to waste a lot of time during intialization, there - // only a small number of samples are available. - // - // Therefore we use a very simple and conservative estimate: - // `let bits_of_entropy = log2(delta_average) / 2`. - // - // The number of rounds `measure_jitter` should run to collect 64 bits - // of entropy is `64 / bits_of_entropy`. - // - // To have smaller rounding errors, intermediate values are multiplied - // by `FACTOR`. To compensate for `log2` and division rounding down, - // add 1. - let delta_average = delta_sum / TESTLOOPCOUNT; - // println!("delta_average: {}", delta_average); - - const FACTOR: u32 = 3; - fn log2(x: u64) -> u32 { 64 - x.leading_zeros() } - - // pow(δ, FACTOR) must be representable; if you have overflow reduce FACTOR - Ok(64 * 2 * FACTOR / (log2(delta_average.pow(FACTOR)) + 1)) - } - - /// Statistical test: return the timer delta of one normal run of the - /// `JitterEntropy` entropy collector. - /// - /// Setting `var_rounds` to `true` will execute the memory access and the - /// CPU jitter noice sources a variable amount of times (just like a real - /// `JitterEntropy` round). - /// - /// Setting `var_rounds` to `false` will execute the noice sources the - /// minimal number of times. This can be used to measure the minimum amount - /// of entropy one round of entropy collector can collect in the worst case. - /// - /// # Example - /// - /// Use `timer_stats` to run the [NIST SP 800-90B Entropy Estimation Suite] - /// (https://github.com/usnistgov/SP800-90B_EntropyAssessment). - /// - /// This is the recommended way to test the quality of `JitterRng`. It - /// should be run before using the RNG on untested hardware, after changes - /// that could effect how the code is optimised, and after major compiler - /// compiler changes, like a new LLVM version. - /// - /// First generate two files `jitter_rng_var.bin` and `jitter_rng_var.min`. - /// - /// Execute `python noniid_main.py -v jitter_rng_var.bin 8`, and validate it - /// with `restart.py -v jitter_rng_var.bin 8 <min-entropy>`. - /// This number is the expected amount of entropy that is at least available - /// for each round of the entropy collector. This number should be greater - /// than the amount estimated with `64 / test_timer()`. - /// - /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and - /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`. - /// This number is the expected amount of entropy that is available in the - /// last 4 bits of the timer delta after running noice sources. Note that - /// a value of 3.70 is the minimum estimated entropy for true randomness. - /// - /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and - /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`. - /// This number is the expected amount of entropy that is available to the - /// entropy collecter if both noice sources only run their minimal number of - /// times. This measures the absolute worst-case, and gives a lower bound - /// for the available entropy. - /// - /// ```rust,no_run - /// use rand::JitterRng; - /// - /// # use std::error::Error; - /// # use std::fs::File; - /// # use std::io::Write; - /// # - /// # fn try_main() -> Result<(), Box<Error>> { - /// fn get_nstime() -> u64 { - /// use std::time::{SystemTime, UNIX_EPOCH}; - /// - /// let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap(); - /// // The correct way to calculate the current time is - /// // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64` - /// // But this is faster, and the difference in terms of entropy is - /// // negligible (log2(10^9) == 29.9). - /// dur.as_secs() << 30 | dur.subsec_nanos() as u64 - /// } - /// - /// // Do not initialize with `JitterRng::new`, but with `new_with_timer`. - /// // 'new' always runst `test_timer`, and can therefore fail to - /// // initialize. We want to be able to get the statistics even when the - /// // timer test fails. - /// let mut rng = JitterRng::new_with_timer(get_nstime); - /// - /// // 1_000_000 results are required for the NIST SP 800-90B Entropy - /// // Estimation Suite - /// // FIXME: this number is smaller here, otherwise the Doc-test is too slow - /// const ROUNDS: usize = 10_000; - /// let mut deltas_variable: Vec<u8> = Vec::with_capacity(ROUNDS); - /// let mut deltas_minimal: Vec<u8> = Vec::with_capacity(ROUNDS); - /// - /// for _ in 0..ROUNDS { - /// deltas_variable.push(rng.timer_stats(true) as u8); - /// deltas_minimal.push(rng.timer_stats(false) as u8); - /// } - /// - /// // Write out after the statistics collection loop, to not disturb the - /// // test results. - /// File::create("jitter_rng_var.bin")?.write(&deltas_variable)?; - /// File::create("jitter_rng_min.bin")?.write(&deltas_minimal)?; - /// # - /// # Ok(()) - /// # } - /// # - /// # fn main() { - /// # try_main().unwrap(); - /// # } - /// ``` - #[cfg(feature="std")] - pub fn timer_stats(&mut self, var_rounds: bool) -> i64 { - let time = platform::get_nstime(); - self.memaccess(var_rounds); - self.lfsr_time(time, var_rounds); - let time2 = platform::get_nstime(); - time2.wrapping_sub(time) as i64 - } -} - -#[cfg(feature="std")] -mod platform { - #[cfg(not(any(target_os = "macos", target_os = "ios", target_os = "windows", all(target_arch = "wasm32", not(target_os = "emscripten")))))] - pub fn get_nstime() -> u64 { - use std::time::{SystemTime, UNIX_EPOCH}; - - let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap(); - // The correct way to calculate the current time is - // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64` - // But this is faster, and the difference in terms of entropy is negligible - // (log2(10^9) == 29.9). - dur.as_secs() << 30 | dur.subsec_nanos() as u64 - } - - #[cfg(any(target_os = "macos", target_os = "ios"))] - pub fn get_nstime() -> u64 { - extern crate libc; - // On Mac OS and iOS std::time::SystemTime only has 1000ns resolution. - // We use `mach_absolute_time` instead. This provides a CPU dependent unit, - // to get real nanoseconds the result should by multiplied by numer/denom - // from `mach_timebase_info`. - // But we are not interested in the exact nanoseconds, just entropy. So we - // use the raw result. - unsafe { libc::mach_absolute_time() } - } - - #[cfg(target_os = "windows")] - pub fn get_nstime() -> u64 { - extern crate winapi; - unsafe { - let mut t = super::mem::zeroed(); - winapi::um::profileapi::QueryPerformanceCounter(&mut t); - *t.QuadPart() as u64 - } - } - - #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] - pub fn get_nstime() -> u64 { - unreachable!() - } -} - -// A function that is opaque to the optimizer to assist in avoiding dead-code -// elimination. Taken from `bencher`. -fn black_box<T>(dummy: T) -> T { - unsafe { - let ret = ptr::read_volatile(&dummy); - mem::forget(dummy); - ret - } -} - -impl Rng for JitterRng { - fn next_u32(&mut self) -> u32 { - // We want to use both parts of the generated entropy - if let Some(high) = self.data_remaining.take() { - high - } else { - let data = self.next_u64(); - self.data_remaining = Some((data >> 32) as u32); - data as u32 - } - } - - fn next_u64(&mut self) -> u64 { - self.gen_entropy() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - let mut left = dest; - while left.len() >= 8 { - let (l, r) = {left}.split_at_mut(8); - left = r; - let chunk: [u8; 8] = unsafe { - mem::transmute(self.next_u64().to_le()) - }; - l.copy_from_slice(&chunk); - } - let n = left.len(); - if n > 0 { - let chunk: [u8; 8] = unsafe { - mem::transmute(self.next_u64().to_le()) - }; - left.copy_from_slice(&chunk[..n]); - } - } -} - -// There are no tests included because (1) this is an "external" RNG, so output -// is not reproducible and (2) `test_timer` *will* fail on some platforms. diff --git a/vendor/rand/src/lib.rs b/vendor/rand/src/lib.rs deleted file mode 100644 index 696ff6f..0000000 --- a/vendor/rand/src/lib.rs +++ /dev/null @@ -1,1220 +0,0 @@ -// Copyright 2013-2017 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. - -//! Utilities for random number generation -//! -//! The key functions are `random()` and `Rng::gen()`. These are polymorphic and -//! so can be used to generate any type that implements `Rand`. Type inference -//! means that often a simple call to `rand::random()` or `rng.gen()` will -//! suffice, but sometimes an annotation is required, e.g. -//! `rand::random::<f64>()`. -//! -//! See the `distributions` submodule for sampling random numbers from -//! distributions like normal and exponential. -//! -//! # Usage -//! -//! This crate is [on crates.io](https://crates.io/crates/rand) and can be -//! used by adding `rand` to the dependencies in your project's `Cargo.toml`. -//! -//! ```toml -//! [dependencies] -//! rand = "0.4" -//! ``` -//! -//! and this to your crate root: -//! -//! ```rust -//! extern crate rand; -//! ``` -//! -//! # Thread-local RNG -//! -//! There is built-in support for a RNG associated with each thread stored -//! in thread-local storage. This RNG can be accessed via `thread_rng`, or -//! used implicitly via `random`. This RNG is normally randomly seeded -//! from an operating-system source of randomness, e.g. `/dev/urandom` on -//! Unix systems, and will automatically reseed itself from this source -//! after generating 32 KiB of random data. -//! -//! # Cryptographic security -//! -//! An application that requires an entropy source for cryptographic purposes -//! must use `OsRng`, which reads randomness from the source that the operating -//! system provides (e.g. `/dev/urandom` on Unixes or `CryptGenRandom()` on -//! Windows). -//! The other random number generators provided by this module are not suitable -//! for such purposes. -//! -//! *Note*: many Unix systems provide `/dev/random` as well as `/dev/urandom`. -//! This module uses `/dev/urandom` for the following reasons: -//! -//! - On Linux, `/dev/random` may block if entropy pool is empty; -//! `/dev/urandom` will not block. This does not mean that `/dev/random` -//! provides better output than `/dev/urandom`; the kernel internally runs a -//! cryptographically secure pseudorandom number generator (CSPRNG) based on -//! entropy pool for random number generation, so the "quality" of -//! `/dev/random` is not better than `/dev/urandom` in most cases. However, -//! this means that `/dev/urandom` can yield somewhat predictable randomness -//! if the entropy pool is very small, such as immediately after first -//! booting. Linux 3.17 added the `getrandom(2)` system call which solves -//! the issue: it blocks if entropy pool is not initialized yet, but it does -//! not block once initialized. `OsRng` tries to use `getrandom(2)` if -//! available, and use `/dev/urandom` fallback if not. If an application -//! does not have `getrandom` and likely to be run soon after first booting, -//! or on a system with very few entropy sources, one should consider using -//! `/dev/random` via `ReadRng`. -//! - On some systems (e.g. FreeBSD, OpenBSD and Mac OS X) there is no -//! difference between the two sources. (Also note that, on some systems -//! e.g. FreeBSD, both `/dev/random` and `/dev/urandom` may block once if -//! the CSPRNG has not seeded yet.) -//! -//! # Examples -//! -//! ```rust -//! use rand::Rng; -//! -//! let mut rng = rand::thread_rng(); -//! if rng.gen() { // random bool -//! println!("i32: {}, u32: {}", rng.gen::<i32>(), rng.gen::<u32>()) -//! } -//! ``` -//! -//! ```rust -//! let tuple = rand::random::<(f64, char)>(); -//! println!("{:?}", tuple) -//! ``` -//! -//! ## Monte Carlo estimation of π -//! -//! For this example, imagine we have a square with sides of length 2 and a unit -//! circle, both centered at the origin. Since the area of a unit circle is π, -//! we have: -//! -//! ```text -//! (area of unit circle) / (area of square) = π / 4 -//! ``` -//! -//! So if we sample many points randomly from the square, roughly π / 4 of them -//! should be inside the circle. -//! -//! We can use the above fact to estimate the value of π: pick many points in -//! the square at random, calculate the fraction that fall within the circle, -//! and multiply this fraction by 4. -//! -//! ``` -//! use rand::distributions::{IndependentSample, Range}; -//! -//! fn main() { -//! let between = Range::new(-1f64, 1.); -//! let mut rng = rand::thread_rng(); -//! -//! let total = 1_000_000; -//! let mut in_circle = 0; -//! -//! for _ in 0..total { -//! let a = between.ind_sample(&mut rng); -//! let b = between.ind_sample(&mut rng); -//! if a*a + b*b <= 1. { -//! in_circle += 1; -//! } -//! } -//! -//! // prints something close to 3.14159... -//! println!("{}", 4. * (in_circle as f64) / (total as f64)); -//! } -//! ``` -//! -//! ## Monty Hall Problem -//! -//! This is a simulation of the [Monty Hall Problem][]: -//! -//! > Suppose you're on a game show, and you're given the choice of three doors: -//! > Behind one door is a car; behind the others, goats. You pick a door, say -//! > No. 1, and the host, who knows what's behind the doors, opens another -//! > door, say No. 3, which has a goat. He then says to you, "Do you want to -//! > pick door No. 2?" Is it to your advantage to switch your choice? -//! -//! The rather unintuitive answer is that you will have a 2/3 chance of winning -//! if you switch and a 1/3 chance of winning if you don't, so it's better to -//! switch. -//! -//! This program will simulate the game show and with large enough simulation -//! steps it will indeed confirm that it is better to switch. -//! -//! [Monty Hall Problem]: http://en.wikipedia.org/wiki/Monty_Hall_problem -//! -//! ``` -//! use rand::Rng; -//! use rand::distributions::{IndependentSample, Range}; -//! -//! struct SimulationResult { -//! win: bool, -//! switch: bool, -//! } -//! -//! // Run a single simulation of the Monty Hall problem. -//! fn simulate<R: Rng>(random_door: &Range<u32>, rng: &mut R) -//! -> SimulationResult { -//! let car = random_door.ind_sample(rng); -//! -//! // This is our initial choice -//! let mut choice = random_door.ind_sample(rng); -//! -//! // The game host opens a door -//! let open = game_host_open(car, choice, rng); -//! -//! // Shall we switch? -//! let switch = rng.gen(); -//! if switch { -//! choice = switch_door(choice, open); -//! } -//! -//! SimulationResult { win: choice == car, switch: switch } -//! } -//! -//! // Returns the door the game host opens given our choice and knowledge of -//! // where the car is. The game host will never open the door with the car. -//! fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 { -//! let choices = free_doors(&[car, choice]); -//! rand::seq::sample_slice(rng, &choices, 1)[0] -//! } -//! -//! // Returns the door we switch to, given our current choice and -//! // the open door. There will only be one valid door. -//! fn switch_door(choice: u32, open: u32) -> u32 { -//! free_doors(&[choice, open])[0] -//! } -//! -//! fn free_doors(blocked: &[u32]) -> Vec<u32> { -//! (0..3).filter(|x| !blocked.contains(x)).collect() -//! } -//! -//! fn main() { -//! // The estimation will be more accurate with more simulations -//! let num_simulations = 10000; -//! -//! let mut rng = rand::thread_rng(); -//! let random_door = Range::new(0, 3); -//! -//! let (mut switch_wins, mut switch_losses) = (0, 0); -//! let (mut keep_wins, mut keep_losses) = (0, 0); -//! -//! println!("Running {} simulations...", num_simulations); -//! for _ in 0..num_simulations { -//! let result = simulate(&random_door, &mut rng); -//! -//! match (result.win, result.switch) { -//! (true, true) => switch_wins += 1, -//! (true, false) => keep_wins += 1, -//! (false, true) => switch_losses += 1, -//! (false, false) => keep_losses += 1, -//! } -//! } -//! -//! let total_switches = switch_wins + switch_losses; -//! let total_keeps = keep_wins + keep_losses; -//! -//! println!("Switched door {} times with {} wins and {} losses", -//! total_switches, switch_wins, switch_losses); -//! -//! println!("Kept our choice {} times with {} wins and {} losses", -//! total_keeps, keep_wins, keep_losses); -//! -//! // With a large number of simulations, the values should converge to -//! // 0.667 and 0.333 respectively. -//! println!("Estimated chance to win if we switch: {}", -//! switch_wins as f32 / total_switches as f32); -//! println!("Estimated chance to win if we don't: {}", -//! keep_wins as f32 / total_keeps as f32); -//! } -//! ``` - -#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png", - html_favicon_url = "https://www.rust-lang.org/favicon.ico", - html_root_url = "https://docs.rs/rand/0.4")] - -#![deny(missing_debug_implementations)] - -#![cfg_attr(not(feature="std"), no_std)] -#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))] -#![cfg_attr(feature = "i128_support", feature(i128_type, i128))] - -#[cfg(feature="std")] extern crate std as core; -#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc; - -#[cfg(target_env = "sgx")] -extern crate rdrand; - -#[cfg(target_env = "sgx")] -extern crate rand_core; - -use core::marker; -use core::mem; -#[cfg(feature="std")] use std::cell::RefCell; -#[cfg(feature="std")] use std::io; -#[cfg(feature="std")] use std::rc::Rc; - -// external rngs -pub use jitter::JitterRng; -#[cfg(feature="std")] pub use os::OsRng; - -// pseudo rngs -pub use isaac::{IsaacRng, Isaac64Rng}; -pub use chacha::ChaChaRng; -pub use prng::XorShiftRng; - -// local use declarations -#[cfg(target_pointer_width = "32")] -use prng::IsaacRng as IsaacWordRng; -#[cfg(target_pointer_width = "64")] -use prng::Isaac64Rng as IsaacWordRng; - -use distributions::{Range, IndependentSample}; -use distributions::range::SampleRange; - -// public modules -pub mod distributions; -pub mod jitter; -#[cfg(feature="std")] pub mod os; -#[cfg(feature="std")] pub mod read; -pub mod reseeding; -#[cfg(any(feature="std", feature = "alloc"))] pub mod seq; - -// These tiny modules are here to avoid API breakage, probably only temporarily -pub mod chacha { - //! The ChaCha random number generator. - pub use prng::ChaChaRng; -} -pub mod isaac { - //! The ISAAC random number generator. - pub use prng::{IsaacRng, Isaac64Rng}; -} - -// private modules -mod rand_impls; -mod prng; - - -/// A type that can be randomly generated using an `Rng`. -/// -/// ## Built-in Implementations -/// -/// This crate implements `Rand` for various primitive types. Assuming the -/// provided `Rng` is well-behaved, these implementations generate values with -/// the following ranges and distributions: -/// -/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed -/// over all values of the type. -/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all -/// code points in the range `0...0x10_FFFF`, except for the range -/// `0xD800...0xDFFF` (the surrogate code points). This includes -/// unassigned/reserved code points. -/// * `bool`: Generates `false` or `true`, each with probability 0.5. -/// * Floating point types (`f32` and `f64`): Uniformly distributed in the -/// half-open range `[0, 1)`. (The [`Open01`], [`Closed01`], [`Exp1`], and -/// [`StandardNormal`] wrapper types produce floating point numbers with -/// alternative ranges or distributions.) -/// -/// [`Open01`]: struct.Open01.html -/// [`Closed01`]: struct.Closed01.html -/// [`Exp1`]: distributions/exponential/struct.Exp1.html -/// [`StandardNormal`]: distributions/normal/struct.StandardNormal.html -/// -/// The following aggregate types also implement `Rand` as long as their -/// component types implement it: -/// -/// * Tuples and arrays: Each element of the tuple or array is generated -/// independently, using its own `Rand` implementation. -/// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a -/// random `T` and returns `Some(T)`. -pub trait Rand : Sized { - /// Generates a random instance of this type using the specified source of - /// randomness. - fn rand<R: Rng>(rng: &mut R) -> Self; -} - -/// A random number generator. -pub trait Rng { - /// Return the next random u32. - /// - /// This rarely needs to be called directly, prefer `r.gen()` to - /// `r.next_u32()`. - // FIXME #rust-lang/rfcs#628: Should be implemented in terms of next_u64 - fn next_u32(&mut self) -> u32; - - /// Return the next random u64. - /// - /// By default this is implemented in terms of `next_u32`. An - /// implementation of this trait must provide at least one of - /// these two methods. Similarly to `next_u32`, this rarely needs - /// to be called directly, prefer `r.gen()` to `r.next_u64()`. - fn next_u64(&mut self) -> u64 { - ((self.next_u32() as u64) << 32) | (self.next_u32() as u64) - } - - /// 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> - /// - /// By default this is implemented in terms of `next_u32`, but a - /// random number generator which can generate numbers satisfying - /// the requirements directly can overload this for performance. - /// It is required that the return value lies in `[0, 1)`. - /// - /// See `Closed01` for the closed interval `[0,1]`, and - /// `Open01` for the open interval `(0,1)`. - fn next_f32(&mut self) -> f32 { - const UPPER_MASK: u32 = 0x3F800000; - const LOWER_MASK: u32 = 0x7FFFFF; - let tmp = UPPER_MASK | (self.next_u32() & LOWER_MASK); - let result: f32 = unsafe { mem::transmute(tmp) }; - result - 1.0 - } - - /// Return the next random f64 selected from the half-open - /// interval `[0, 1)`. - /// - /// By default this is implemented in terms of `next_u64`, but a - /// random number generator which can generate numbers satisfying - /// the requirements directly can overload this for performance. - /// It is required that the return value lies in `[0, 1)`. - /// - /// See `Closed01` for the closed interval `[0,1]`, and - /// `Open01` for the open interval `(0,1)`. - fn next_f64(&mut self) -> f64 { - const UPPER_MASK: u64 = 0x3FF0000000000000; - const LOWER_MASK: u64 = 0xFFFFFFFFFFFFF; - let tmp = UPPER_MASK | (self.next_u64() & LOWER_MASK); - let result: f64 = unsafe { mem::transmute(tmp) }; - result - 1.0 - } - - /// Fill `dest` with random data. - /// - /// This has a default implementation in terms of `next_u64` and - /// `next_u32`, but should be overridden by implementations that - /// offer a more efficient solution than just calling those - /// methods repeatedly. - /// - /// This method does *not* have a requirement to bear any fixed - /// relationship to the other methods, for example, it does *not* - /// have to result in the same output as progressively filling - /// `dest` with `self.gen::<u8>()`, and any such behaviour should - /// not be relied upon. - /// - /// This method should guarantee that `dest` is entirely filled - /// with new data, and may panic if this is impossible - /// (e.g. reading past the end of a file that is being used as the - /// source of randomness). - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut v = [0u8; 13579]; - /// thread_rng().fill_bytes(&mut v); - /// println!("{:?}", &v[..]); - /// ``` - fn fill_bytes(&mut self, dest: &mut [u8]) { - // this could, in theory, be done by transmuting dest to a - // [u64], but this is (1) likely to be undefined behaviour for - // LLVM, (2) has to be very careful about alignment concerns, - // (3) adds more `unsafe` that needs to be checked, (4) - // probably doesn't give much performance gain if - // optimisations are on. - let mut count = 0; - let mut num = 0; - for byte in dest.iter_mut() { - if count == 0 { - // we could micro-optimise here by generating a u32 if - // we only need a few more bytes to fill the vector - // (i.e. at most 4). - num = self.next_u64(); - count = 8; - } - - *byte = (num & 0xff) as u8; - num >>= 8; - count -= 1; - } - } - - /// Return a random value of a `Rand` type. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let x: u32 = rng.gen(); - /// println!("{}", x); - /// println!("{:?}", rng.gen::<(f64, bool)>()); - /// ``` - #[inline(always)] - fn gen<T: Rand>(&mut self) -> T where Self: Sized { - Rand::rand(self) - } - - /// Return an iterator that will yield an infinite number of randomly - /// generated items. - /// - /// # Example - /// - /// ``` - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>(); - /// println!("{:?}", x); - /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5) - /// .collect::<Vec<(f64, bool)>>()); - /// ``` - fn gen_iter<'a, T: Rand>(&'a mut self) -> Generator<'a, T, Self> where Self: Sized { - Generator { rng: self, _marker: marker::PhantomData } - } - - /// Generate a random value in the range [`low`, `high`). - /// - /// This is a convenience wrapper around - /// `distributions::Range`. If this function will be called - /// repeatedly with the same arguments, one should use `Range`, as - /// that will amortize the computations that allow for perfect - /// uniformity, as they only happen on initialization. - /// - /// # Panics - /// - /// Panics if `low >= high`. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let n: u32 = rng.gen_range(0, 10); - /// println!("{}", n); - /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64); - /// println!("{}", m); - /// ``` - fn gen_range<T: PartialOrd + SampleRange>(&mut self, low: T, high: T) -> T where Self: Sized { - assert!(low < high, "Rng.gen_range called with low >= high"); - Range::new(low, high).ind_sample(self) - } - - /// Return a bool with a 1 in n chance of true - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// println!("{}", rng.gen_weighted_bool(3)); - /// ``` - fn gen_weighted_bool(&mut self, n: u32) -> bool where Self: Sized { - n <= 1 || self.gen_range(0, n) == 0 - } - - /// Return an iterator of random characters from the set A-Z,a-z,0-9. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let s: String = thread_rng().gen_ascii_chars().take(10).collect(); - /// println!("{}", s); - /// ``` - fn gen_ascii_chars<'a>(&'a mut self) -> AsciiGenerator<'a, Self> where Self: Sized { - AsciiGenerator { rng: self } - } - - /// Return a random element from `values`. - /// - /// Return `None` if `values` is empty. - /// - /// # Example - /// - /// ``` - /// use rand::{thread_rng, Rng}; - /// - /// let choices = [1, 2, 4, 8, 16, 32]; - /// let mut rng = thread_rng(); - /// println!("{:?}", rng.choose(&choices)); - /// assert_eq!(rng.choose(&choices[..0]), None); - /// ``` - fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> where Self: Sized { - if values.is_empty() { - None - } else { - Some(&values[self.gen_range(0, values.len())]) - } - } - - /// Return a mutable pointer to a random element from `values`. - /// - /// Return `None` if `values` is empty. - fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> where Self: Sized { - if values.is_empty() { - None - } else { - let len = values.len(); - Some(&mut values[self.gen_range(0, len)]) - } - } - - /// Shuffle a mutable slice in place. - /// - /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm) - /// which produces an unbiased permutation. - /// - /// # Example - /// - /// ```rust - /// use rand::{thread_rng, Rng}; - /// - /// let mut rng = thread_rng(); - /// let mut y = [1, 2, 3]; - /// rng.shuffle(&mut y); - /// println!("{:?}", y); - /// rng.shuffle(&mut y); - /// println!("{:?}", y); - /// ``` - fn shuffle<T>(&mut self, values: &mut [T]) where Self: Sized { - let mut i = values.len(); - while i >= 2 { - // invariant: elements with index >= i have been locked in place. - i -= 1; - // lock element i in place. - values.swap(i, self.gen_range(0, i + 1)); - } - } -} - -impl<'a, R: ?Sized> Rng for &'a mut R where R: Rng { - fn next_u32(&mut self) -> u32 { - (**self).next_u32() - } - - fn next_u64(&mut self) -> u64 { - (**self).next_u64() - } - - fn next_f32(&mut self) -> f32 { - (**self).next_f32() - } - - fn next_f64(&mut self) -> f64 { - (**self).next_f64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - (**self).fill_bytes(dest) - } -} - -#[cfg(feature="std")] -impl<R: ?Sized> Rng for Box<R> where R: Rng { - fn next_u32(&mut self) -> u32 { - (**self).next_u32() - } - - fn next_u64(&mut self) -> u64 { - (**self).next_u64() - } - - fn next_f32(&mut self) -> f32 { - (**self).next_f32() - } - - fn next_f64(&mut self) -> f64 { - (**self).next_f64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - (**self).fill_bytes(dest) - } -} - -/// Iterator which will generate a stream of random items. -/// -/// This iterator is created via the [`gen_iter`] method on [`Rng`]. -/// -/// [`gen_iter`]: trait.Rng.html#method.gen_iter -/// [`Rng`]: trait.Rng.html -#[derive(Debug)] -pub struct Generator<'a, T, R:'a> { - rng: &'a mut R, - _marker: marker::PhantomData<fn() -> T>, -} - -impl<'a, T: Rand, R: Rng> Iterator for Generator<'a, T, R> { - type Item = T; - - fn next(&mut self) -> Option<T> { - Some(self.rng.gen()) - } -} - -/// Iterator which will continuously generate random ascii characters. -/// -/// This iterator is created via the [`gen_ascii_chars`] method on [`Rng`]. -/// -/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars -/// [`Rng`]: trait.Rng.html -#[derive(Debug)] -pub struct AsciiGenerator<'a, R:'a> { - rng: &'a mut R, -} - -impl<'a, R: Rng> Iterator for AsciiGenerator<'a, R> { - type Item = char; - - fn next(&mut self) -> Option<char> { - const GEN_ASCII_STR_CHARSET: &'static [u8] = - b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\ - abcdefghijklmnopqrstuvwxyz\ - 0123456789"; - Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char) - } -} - -/// A random number generator that can be explicitly seeded to produce -/// the same stream of randomness multiple times. -pub trait SeedableRng<Seed>: Rng { - /// Reseed an RNG with the given seed. - /// - /// # Example - /// - /// ```rust - /// use rand::{Rng, SeedableRng, StdRng}; - /// - /// let seed: &[_] = &[1, 2, 3, 4]; - /// let mut rng: StdRng = SeedableRng::from_seed(seed); - /// println!("{}", rng.gen::<f64>()); - /// rng.reseed(&[5, 6, 7, 8]); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn reseed(&mut self, Seed); - - /// Create a new RNG with the given seed. - /// - /// # Example - /// - /// ```rust - /// use rand::{Rng, SeedableRng, StdRng}; - /// - /// let seed: &[_] = &[1, 2, 3, 4]; - /// let mut rng: StdRng = SeedableRng::from_seed(seed); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn from_seed(seed: Seed) -> Self; -} - -/// A wrapper for generating floating point numbers uniformly in the -/// open interval `(0,1)` (not including either endpoint). -/// -/// Use `Closed01` for the closed interval `[0,1]`, and the default -/// `Rand` implementation for `f32` and `f64` for the half-open -/// `[0,1)`. -/// -/// # Example -/// ```rust -/// use rand::{random, Open01}; -/// -/// let Open01(val) = random::<Open01<f32>>(); -/// println!("f32 from (0,1): {}", val); -/// ``` -#[derive(Debug)] -pub struct Open01<F>(pub F); - -/// A wrapper for generating floating point numbers uniformly in the -/// closed interval `[0,1]` (including both endpoints). -/// -/// Use `Open01` for the closed interval `(0,1)`, and the default -/// `Rand` implementation of `f32` and `f64` for the half-open -/// `[0,1)`. -/// -/// # Example -/// -/// ```rust -/// use rand::{random, Closed01}; -/// -/// let Closed01(val) = random::<Closed01<f32>>(); -/// println!("f32 from [0,1]: {}", val); -/// ``` -#[derive(Debug)] -pub struct Closed01<F>(pub F); - -/// The standard RNG. This is designed to be efficient on the current -/// platform. -#[derive(Copy, Clone, Debug)] -pub struct StdRng { - rng: IsaacWordRng, -} - -impl StdRng { - /// Create a randomly seeded instance of `StdRng`. - /// - /// This is a very expensive operation as it has to read - /// randomness from the operating system and use this in an - /// expensive seeding operation. If one is only generating a small - /// number of random numbers, or doesn't need the utmost speed for - /// generating each number, `thread_rng` and/or `random` may be more - /// appropriate. - /// - /// Reading the randomness from the OS may fail, and any error is - /// propagated via the `io::Result` return value. - #[cfg(feature="std")] - pub fn new() -> io::Result<StdRng> { - match OsRng::new() { - Ok(mut r) => Ok(StdRng { rng: r.gen() }), - Err(e1) => { - match JitterRng::new() { - Ok(mut r) => Ok(StdRng { rng: r.gen() }), - Err(_) => { - Err(e1) - } - } - } - } - } -} - -impl Rng for StdRng { - #[inline] - fn next_u32(&mut self) -> u32 { - self.rng.next_u32() - } - - #[inline] - fn next_u64(&mut self) -> u64 { - self.rng.next_u64() - } -} - -impl<'a> SeedableRng<&'a [usize]> for StdRng { - fn reseed(&mut self, seed: &'a [usize]) { - // the internal RNG can just be seeded from the above - // randomness. - self.rng.reseed(unsafe {mem::transmute(seed)}) - } - - fn from_seed(seed: &'a [usize]) -> StdRng { - StdRng { rng: SeedableRng::from_seed(unsafe {mem::transmute(seed)}) } - } -} - -/// Create a weak random number generator with a default algorithm and seed. -/// -/// It returns the fastest `Rng` algorithm currently available in Rust without -/// consideration for cryptography or security. If you require a specifically -/// seeded `Rng` for consistency over time you should pick one algorithm and -/// create the `Rng` yourself. -/// -/// This will seed the generator with randomness from thread_rng. -#[cfg(feature="std")] -pub fn weak_rng() -> XorShiftRng { - thread_rng().gen() -} - -/// Controls how the thread-local RNG is reseeded. -#[cfg(feature="std")] -#[derive(Debug)] -struct ThreadRngReseeder; - -#[cfg(feature="std")] -impl reseeding::Reseeder<StdRng> for ThreadRngReseeder { - fn reseed(&mut self, rng: &mut StdRng) { - match StdRng::new() { - Ok(r) => *rng = r, - Err(e) => panic!("No entropy available: {}", e), - } - } -} -#[cfg(feature="std")] -const THREAD_RNG_RESEED_THRESHOLD: u64 = 32_768; -#[cfg(feature="std")] -type ThreadRngInner = reseeding::ReseedingRng<StdRng, ThreadRngReseeder>; - -/// The thread-local RNG. -#[cfg(feature="std")] -#[derive(Clone, Debug)] -pub struct ThreadRng { - rng: Rc<RefCell<ThreadRngInner>>, -} - -/// Retrieve the lazily-initialized thread-local random number -/// generator, seeded by the system. Intended to be used in method -/// chaining style, e.g. `thread_rng().gen::<i32>()`. -/// -/// After generating a certain amount of randomness, the RNG will reseed itself -/// from the operating system or, if the operating system RNG returns an error, -/// a seed based on the current system time. -/// -/// The internal RNG used is platform and architecture dependent, even -/// if the operating system random number generator is rigged to give -/// the same sequence always. If absolute consistency is required, -/// explicitly select an RNG, e.g. `IsaacRng` or `Isaac64Rng`. -#[cfg(feature="std")] -pub fn thread_rng() -> ThreadRng { - // used to make space in TLS for a random number generator - thread_local!(static THREAD_RNG_KEY: Rc<RefCell<ThreadRngInner>> = { - let r = match StdRng::new() { - Ok(r) => r, - Err(e) => panic!("No entropy available: {}", e), - }; - let rng = reseeding::ReseedingRng::new(r, - THREAD_RNG_RESEED_THRESHOLD, - ThreadRngReseeder); - Rc::new(RefCell::new(rng)) - }); - - ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) } -} - -#[cfg(feature="std")] -impl Rng for ThreadRng { - fn next_u32(&mut self) -> u32 { - self.rng.borrow_mut().next_u32() - } - - fn next_u64(&mut self) -> u64 { - self.rng.borrow_mut().next_u64() - } - - #[inline] - fn fill_bytes(&mut self, bytes: &mut [u8]) { - self.rng.borrow_mut().fill_bytes(bytes) - } -} - -/// Generates a random value using the thread-local random number generator. -/// -/// `random()` can generate various types of random things, and so may require -/// type hinting to generate the specific type you want. -/// -/// This function uses the thread local random number generator. This means -/// that if you're calling `random()` in a loop, caching the generator can -/// increase performance. An example is shown below. -/// -/// # Examples -/// -/// ``` -/// let x = rand::random::<u8>(); -/// println!("{}", x); -/// -/// let y = rand::random::<f64>(); -/// println!("{}", y); -/// -/// if rand::random() { // generates a boolean -/// println!("Better lucky than good!"); -/// } -/// ``` -/// -/// Caching the thread local random number generator: -/// -/// ``` -/// use rand::Rng; -/// -/// let mut v = vec![1, 2, 3]; -/// -/// for x in v.iter_mut() { -/// *x = rand::random() -/// } -/// -/// // can be made faster by caching thread_rng -/// -/// let mut rng = rand::thread_rng(); -/// -/// for x in v.iter_mut() { -/// *x = rng.gen(); -/// } -/// ``` -#[cfg(feature="std")] -#[inline] -pub fn random<T: Rand>() -> T { - thread_rng().gen() -} - -/// DEPRECATED: use `seq::sample_iter` instead. -/// -/// Randomly sample up to `amount` elements from a finite iterator. -/// The order of elements in the sample is not random. -/// -/// # Example -/// -/// ```rust -/// use rand::{thread_rng, sample}; -/// -/// let mut rng = thread_rng(); -/// let sample = sample(&mut rng, 1..100, 5); -/// println!("{:?}", sample); -/// ``` -#[cfg(feature="std")] -#[inline(always)] -#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")] -pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T> - where I: IntoIterator<Item=T>, - R: Rng, -{ - // the legacy sample didn't care whether amount was met - seq::sample_iter(rng, iterable, amount) - .unwrap_or_else(|e| e) -} - -#[cfg(test)] -mod test { - use super::{Rng, thread_rng, random, SeedableRng, StdRng, weak_rng}; - use std::iter::repeat; - - pub struct MyRng<R> { inner: R } - - impl<R: Rng> Rng for MyRng<R> { - fn next_u32(&mut self) -> u32 { - fn next<T: Rng>(t: &mut T) -> u32 { - t.next_u32() - } - next(&mut self.inner) - } - } - - pub fn rng() -> MyRng<::ThreadRng> { - MyRng { inner: ::thread_rng() } - } - - struct ConstRng { i: u64 } - impl Rng for ConstRng { - fn next_u32(&mut self) -> u32 { self.i as u32 } - fn next_u64(&mut self) -> u64 { self.i } - - // no fill_bytes on purpose - } - - pub fn iter_eq<I, J>(i: I, j: J) -> bool - where I: IntoIterator, - J: IntoIterator<Item=I::Item>, - I::Item: Eq - { - // make sure the iterators have equal length - let mut i = i.into_iter(); - let mut j = j.into_iter(); - loop { - match (i.next(), j.next()) { - (Some(ref ei), Some(ref ej)) if ei == ej => { } - (None, None) => return true, - _ => return false, - } - } - } - - #[test] - fn test_fill_bytes_default() { - let mut r = ConstRng { i: 0x11_22_33_44_55_66_77_88 }; - - // check every remainder mod 8, both in small and big vectors. - let lengths = [0, 1, 2, 3, 4, 5, 6, 7, - 80, 81, 82, 83, 84, 85, 86, 87]; - for &n in lengths.iter() { - let mut v = repeat(0u8).take(n).collect::<Vec<_>>(); - r.fill_bytes(&mut v); - - // use this to get nicer error messages. - for (i, &byte) in v.iter().enumerate() { - if byte == 0 { - panic!("byte {} of {} is zero", i, n) - } - } - } - } - - #[test] - fn test_gen_range() { - let mut r = thread_rng(); - for _ in 0..1000 { - let a = r.gen_range(-3, 42); - assert!(a >= -3 && a < 42); - assert_eq!(r.gen_range(0, 1), 0); - assert_eq!(r.gen_range(-12, -11), -12); - } - - for _ in 0..1000 { - let a = r.gen_range(10, 42); - assert!(a >= 10 && a < 42); - assert_eq!(r.gen_range(0, 1), 0); - assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000); - } - - } - - #[test] - #[should_panic] - fn test_gen_range_panic_int() { - let mut r = thread_rng(); - r.gen_range(5, -2); - } - - #[test] - #[should_panic] - fn test_gen_range_panic_usize() { - let mut r = thread_rng(); - r.gen_range(5, 2); - } - - #[test] - fn test_gen_weighted_bool() { - let mut r = thread_rng(); - assert_eq!(r.gen_weighted_bool(0), true); - assert_eq!(r.gen_weighted_bool(1), true); - } - - #[test] - fn test_gen_ascii_str() { - let mut r = thread_rng(); - assert_eq!(r.gen_ascii_chars().take(0).count(), 0); - assert_eq!(r.gen_ascii_chars().take(10).count(), 10); - assert_eq!(r.gen_ascii_chars().take(16).count(), 16); - } - - #[test] - fn test_gen_vec() { - let mut r = thread_rng(); - assert_eq!(r.gen_iter::<u8>().take(0).count(), 0); - assert_eq!(r.gen_iter::<u8>().take(10).count(), 10); - assert_eq!(r.gen_iter::<f64>().take(16).count(), 16); - } - - #[test] - fn test_choose() { - let mut r = thread_rng(); - assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1)); - - let v: &[isize] = &[]; - assert_eq!(r.choose(v), None); - } - - #[test] - fn test_shuffle() { - let mut r = thread_rng(); - let empty: &mut [isize] = &mut []; - r.shuffle(empty); - let mut one = [1]; - r.shuffle(&mut one); - let b: &[_] = &[1]; - assert_eq!(one, b); - - let mut two = [1, 2]; - r.shuffle(&mut two); - assert!(two == [1, 2] || two == [2, 1]); - - let mut x = [1, 1, 1]; - r.shuffle(&mut x); - let b: &[_] = &[1, 1, 1]; - assert_eq!(x, b); - } - - #[test] - fn test_thread_rng() { - let mut r = thread_rng(); - r.gen::<i32>(); - let mut v = [1, 1, 1]; - r.shuffle(&mut v); - let b: &[_] = &[1, 1, 1]; - assert_eq!(v, b); - assert_eq!(r.gen_range(0, 1), 0); - } - - #[test] - fn test_rng_trait_object() { - let mut rng = thread_rng(); - { - let mut r = &mut rng as &mut Rng; - r.next_u32(); - (&mut r).gen::<i32>(); - let mut v = [1, 1, 1]; - (&mut r).shuffle(&mut v); - let b: &[_] = &[1, 1, 1]; - assert_eq!(v, b); - assert_eq!((&mut r).gen_range(0, 1), 0); - } - { - let mut r = Box::new(rng) as Box<Rng>; - r.next_u32(); - r.gen::<i32>(); - let mut v = [1, 1, 1]; - r.shuffle(&mut v); - let b: &[_] = &[1, 1, 1]; - assert_eq!(v, b); - assert_eq!(r.gen_range(0, 1), 0); - } - } - - #[test] - fn test_random() { - // not sure how to test this aside from just getting some values - let _n : usize = random(); - let _f : f32 = random(); - let _o : Option<Option<i8>> = random(); - let _many : ((), - (usize, - isize, - Option<(u32, (bool,))>), - (u8, i8, u16, i16, u32, i32, u64, i64), - (f32, (f64, (f64,)))) = random(); - } - - #[test] - fn test_std_rng_seeded() { - let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - let mut ra: StdRng = SeedableRng::from_seed(&s[..]); - let mut rb: StdRng = SeedableRng::from_seed(&s[..]); - assert!(iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_std_rng_reseed() { - let s = thread_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - let mut r: StdRng = SeedableRng::from_seed(&s[..]); - let string1 = r.gen_ascii_chars().take(100).collect::<String>(); - - r.reseed(&s); - - let string2 = r.gen_ascii_chars().take(100).collect::<String>(); - assert_eq!(string1, string2); - } - - #[test] - fn test_weak_rng() { - let s = weak_rng().gen_iter::<usize>().take(256).collect::<Vec<usize>>(); - let mut ra: StdRng = SeedableRng::from_seed(&s[..]); - let mut rb: StdRng = SeedableRng::from_seed(&s[..]); - assert!(iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } -} diff --git a/vendor/rand/src/os.rs b/vendor/rand/src/os.rs deleted file mode 100644 index c33a8cb..0000000 --- a/vendor/rand/src/os.rs +++ /dev/null @@ -1,656 +0,0 @@ -// Copyright 2013-2015 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. - -//! Interfaces to the operating system provided random number -//! generators. - -use std::{io, fmt}; - -#[cfg(not(target_env = "sgx"))] -use std::mem; - -use Rng; - -/// A random number generator that retrieves randomness straight from -/// the operating system. Platform sources: -/// -/// - Unix-like systems (Linux, Android, Mac OSX): read directly from -/// `/dev/urandom`, or from `getrandom(2)` system call if available. -/// - OpenBSD: calls `getentropy(2)` -/// - FreeBSD: uses the `kern.arandom` `sysctl(2)` mib -/// - Windows: calls `RtlGenRandom`, exported from `advapi32.dll` as -/// `SystemFunction036`. -/// - iOS: calls SecRandomCopyBytes as /dev/(u)random is sandboxed. -/// - PNaCl: calls into the `nacl-irt-random-0.1` IRT interface. -/// -/// This usually does not block. On some systems (e.g. FreeBSD, OpenBSD, -/// Max OS X, and modern Linux) this may block very early in the init -/// process, if the CSPRNG has not been seeded yet.[1] -/// -/// [1] See <https://www.python.org/dev/peps/pep-0524/> for a more -/// in-depth discussion. -pub struct OsRng(imp::OsRng); - -impl OsRng { - /// Create a new `OsRng`. - pub fn new() -> io::Result<OsRng> { - imp::OsRng::new().map(OsRng) - } -} - -impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { self.0.next_u32() } - fn next_u64(&mut self) -> u64 { self.0.next_u64() } - fn fill_bytes(&mut self, v: &mut [u8]) { self.0.fill_bytes(v) } -} - -impl fmt::Debug for OsRng { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "OsRng {{}}") - } -} - -#[cfg(not(target_env = "sgx"))] -fn next_u32(fill_buf: &mut FnMut(&mut [u8])) -> u32 { - let mut buf: [u8; 4] = [0; 4]; - fill_buf(&mut buf); - unsafe { mem::transmute::<[u8; 4], u32>(buf) } -} - -#[cfg(not(target_env = "sgx"))] -fn next_u64(fill_buf: &mut FnMut(&mut [u8])) -> u64 { - let mut buf: [u8; 8] = [0; 8]; - fill_buf(&mut buf); - unsafe { mem::transmute::<[u8; 8], u64>(buf) } -} - -#[cfg(all(unix, not(target_os = "ios"), - not(target_os = "nacl"), - not(target_os = "freebsd"), - not(target_os = "fuchsia"), - not(target_os = "openbsd"), - not(target_os = "redox")))] -mod imp { - extern crate libc; - - use super::{next_u32, next_u64}; - use self::OsRngInner::*; - - use std::io; - use std::fs::File; - use Rng; - use read::ReadRng; - - #[cfg(all(target_os = "linux", - any(target_arch = "x86_64", - target_arch = "x86", - target_arch = "arm", - target_arch = "aarch64", - target_arch = "powerpc")))] - fn getrandom(buf: &mut [u8]) -> libc::c_long { - extern "C" { - fn syscall(number: libc::c_long, ...) -> libc::c_long; - } - - #[cfg(target_arch = "x86_64")] - const NR_GETRANDOM: libc::c_long = 318; - #[cfg(target_arch = "x86")] - const NR_GETRANDOM: libc::c_long = 355; - #[cfg(target_arch = "arm")] - const NR_GETRANDOM: libc::c_long = 384; - #[cfg(target_arch = "aarch64")] - const NR_GETRANDOM: libc::c_long = 278; - #[cfg(target_arch = "powerpc")] - const NR_GETRANDOM: libc::c_long = 359; - - unsafe { - syscall(NR_GETRANDOM, buf.as_mut_ptr(), buf.len(), 0) - } - } - - #[cfg(not(all(target_os = "linux", - any(target_arch = "x86_64", - target_arch = "x86", - target_arch = "arm", - target_arch = "aarch64", - target_arch = "powerpc"))))] - fn getrandom(_buf: &mut [u8]) -> libc::c_long { -1 } - - fn getrandom_fill_bytes(v: &mut [u8]) { - let mut read = 0; - let len = v.len(); - while read < len { - let result = getrandom(&mut v[read..]); - if result == -1 { - let err = io::Error::last_os_error(); - if err.kind() == io::ErrorKind::Interrupted { - continue - } else { - panic!("unexpected getrandom error: {}", err); - } - } else { - read += result as usize; - } - } - } - - #[cfg(all(target_os = "linux", - any(target_arch = "x86_64", - target_arch = "x86", - target_arch = "arm", - target_arch = "aarch64", - target_arch = "powerpc")))] - fn is_getrandom_available() -> bool { - use std::sync::atomic::{AtomicBool, ATOMIC_BOOL_INIT, Ordering}; - use std::sync::{Once, ONCE_INIT}; - - static CHECKER: Once = ONCE_INIT; - static AVAILABLE: AtomicBool = ATOMIC_BOOL_INIT; - - CHECKER.call_once(|| { - let mut buf: [u8; 0] = []; - let result = getrandom(&mut buf); - let available = if result == -1 { - let err = io::Error::last_os_error().raw_os_error(); - err != Some(libc::ENOSYS) - } else { - true - }; - AVAILABLE.store(available, Ordering::Relaxed); - }); - - AVAILABLE.load(Ordering::Relaxed) - } - - #[cfg(not(all(target_os = "linux", - any(target_arch = "x86_64", - target_arch = "x86", - target_arch = "arm", - target_arch = "aarch64", - target_arch = "powerpc"))))] - fn is_getrandom_available() -> bool { false } - - pub struct OsRng { - inner: OsRngInner, - } - - enum OsRngInner { - OsGetrandomRng, - OsReadRng(ReadRng<File>), - } - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - if is_getrandom_available() { - return Ok(OsRng { inner: OsGetrandomRng }); - } - - let reader = try!(File::open("/dev/urandom")); - let reader_rng = ReadRng::new(reader); - - Ok(OsRng { inner: OsReadRng(reader_rng) }) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - match self.inner { - OsGetrandomRng => next_u32(&mut getrandom_fill_bytes), - OsReadRng(ref mut rng) => rng.next_u32(), - } - } - fn next_u64(&mut self) -> u64 { - match self.inner { - OsGetrandomRng => next_u64(&mut getrandom_fill_bytes), - OsReadRng(ref mut rng) => rng.next_u64(), - } - } - fn fill_bytes(&mut self, v: &mut [u8]) { - match self.inner { - OsGetrandomRng => getrandom_fill_bytes(v), - OsReadRng(ref mut rng) => rng.fill_bytes(v) - } - } - } -} - -#[cfg(target_os = "ios")] -mod imp { - extern crate libc; - - use super::{next_u32, next_u64}; - - use std::io; - use Rng; - use self::libc::{c_int, size_t}; - - #[derive(Debug)] - pub struct OsRng; - - enum SecRandom {} - - #[allow(non_upper_case_globals)] - const kSecRandomDefault: *const SecRandom = 0 as *const SecRandom; - - #[link(name = "Security", kind = "framework")] - extern { - fn SecRandomCopyBytes(rnd: *const SecRandom, - count: size_t, bytes: *mut u8) -> c_int; - } - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Ok(OsRng) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - let ret = unsafe { - SecRandomCopyBytes(kSecRandomDefault, v.len() as size_t, v.as_mut_ptr()) - }; - if ret == -1 { - panic!("couldn't generate random bytes: {}", io::Error::last_os_error()); - } - } - } -} - -#[cfg(target_os = "freebsd")] -mod imp { - extern crate libc; - - use std::{io, ptr}; - use Rng; - - use super::{next_u32, next_u64}; - - #[derive(Debug)] - pub struct OsRng; - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Ok(OsRng) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - let mib = [libc::CTL_KERN, libc::KERN_ARND]; - // kern.arandom permits a maximum buffer size of 256 bytes - for s in v.chunks_mut(256) { - let mut s_len = s.len(); - let ret = unsafe { - libc::sysctl(mib.as_ptr(), mib.len() as libc::c_uint, - s.as_mut_ptr() as *mut _, &mut s_len, - ptr::null(), 0) - }; - if ret == -1 || s_len != s.len() { - panic!("kern.arandom sysctl failed! (returned {}, s.len() {}, oldlenp {})", - ret, s.len(), s_len); - } - } - } - } -} - -#[cfg(target_os = "openbsd")] -mod imp { - extern crate libc; - - use std::io; - use Rng; - - use super::{next_u32, next_u64}; - - #[derive(Debug)] - pub struct OsRng; - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Ok(OsRng) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - // getentropy(2) permits a maximum buffer size of 256 bytes - for s in v.chunks_mut(256) { - let ret = unsafe { - libc::getentropy(s.as_mut_ptr() as *mut libc::c_void, s.len()) - }; - if ret == -1 { - let err = io::Error::last_os_error(); - panic!("getentropy failed: {}", err); - } - } - } - } -} - -#[cfg(target_os = "redox")] -mod imp { - use std::io; - use std::fs::File; - use Rng; - use read::ReadRng; - - #[derive(Debug)] - pub struct OsRng { - inner: ReadRng<File>, - } - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - let reader = try!(File::open("rand:")); - let reader_rng = ReadRng::new(reader); - - Ok(OsRng { inner: reader_rng }) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - self.inner.next_u32() - } - fn next_u64(&mut self) -> u64 { - self.inner.next_u64() - } - fn fill_bytes(&mut self, v: &mut [u8]) { - self.inner.fill_bytes(v) - } - } -} - -#[cfg(target_os = "fuchsia")] -mod imp { - extern crate fuchsia_cprng; - - use std::io; - use Rng; - - use super::{next_u32, next_u64}; - - #[derive(Debug)] - pub struct OsRng; - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Ok(OsRng) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - fuchsia_cprng::cprng_draw(v); - } - } -} - -#[cfg(windows)] -mod imp { - extern crate winapi; - - use std::io; - use Rng; - - use super::{next_u32, next_u64}; - - use self::winapi::shared::minwindef::ULONG; - use self::winapi::um::ntsecapi::RtlGenRandom; - use self::winapi::um::winnt::PVOID; - - #[derive(Debug)] - pub struct OsRng; - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Ok(OsRng) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - // RtlGenRandom takes an ULONG (u32) for the length so we need to - // split up the buffer. - for slice in v.chunks_mut(<ULONG>::max_value() as usize) { - let ret = unsafe { - RtlGenRandom(slice.as_mut_ptr() as PVOID, slice.len() as ULONG) - }; - if ret == 0 { - panic!("couldn't generate random bytes: {}", - io::Error::last_os_error()); - } - } - } - } -} - -#[cfg(target_os = "nacl")] -mod imp { - extern crate libc; - - use std::io; - use std::mem; - use Rng; - - use super::{next_u32, next_u64}; - - #[derive(Debug)] - pub struct OsRng(extern fn(dest: *mut libc::c_void, - bytes: libc::size_t, - read: *mut libc::size_t) -> libc::c_int); - - extern { - fn nacl_interface_query(name: *const libc::c_char, - table: *mut libc::c_void, - table_size: libc::size_t) -> libc::size_t; - } - - const INTERFACE: &'static [u8] = b"nacl-irt-random-0.1\0"; - - #[repr(C)] - struct NaClIRTRandom { - get_random_bytes: Option<extern fn(dest: *mut libc::c_void, - bytes: libc::size_t, - read: *mut libc::size_t) -> libc::c_int>, - } - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - let mut iface = NaClIRTRandom { - get_random_bytes: None, - }; - let result = unsafe { - nacl_interface_query(INTERFACE.as_ptr() as *const _, - mem::transmute(&mut iface), - mem::size_of::<NaClIRTRandom>() as libc::size_t) - }; - if result != 0 { - assert!(iface.get_random_bytes.is_some()); - let result = OsRng(iface.get_random_bytes.take().unwrap()); - Ok(result) - } else { - let error = io::ErrorKind::NotFound; - let error = io::Error::new(error, "IRT random interface missing"); - Err(error) - } - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - next_u32(&mut |v| self.fill_bytes(v)) - } - fn next_u64(&mut self) -> u64 { - next_u64(&mut |v| self.fill_bytes(v)) - } - fn fill_bytes(&mut self, v: &mut [u8]) { - let mut read = 0; - loop { - let mut r: libc::size_t = 0; - let len = v.len(); - let error = (self.0)(v[read..].as_mut_ptr() as *mut _, - (len - read) as libc::size_t, - &mut r as *mut _); - assert!(error == 0, "`get_random_bytes` failed!"); - read += r as usize; - - if read >= v.len() { break; } - } - } - } -} - -#[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] -mod imp { - use std::io; - use Rng; - - #[derive(Debug)] - pub struct OsRng; - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - Err(io::Error::new(io::ErrorKind::Other, "Not supported")) - } - } - - impl Rng for OsRng { - fn next_u32(&mut self) -> u32 { - panic!("Not supported") - } - } -} - -#[cfg(target_env = "sgx")] -mod imp { - use rdrand::RdRand; - use std::io; - use rand_core::RngCore; - - pub struct OsRng{ - gen: RdRand - } - - impl OsRng { - pub fn new() -> io::Result<OsRng> { - match RdRand::new() { - Ok(rng) => Ok(OsRng { gen: rng }), - Err(_) => Err(io::Error::new(io::ErrorKind::Other, "Not supported")) - } - } - - pub(crate) fn next_u32(&mut self) -> u32 { - match self.gen.try_next_u32() { - Some(n) => n, - None => panic!("Non-recoverable hardware failure has occured") - } - } - - pub(crate) fn next_u64(&mut self) -> u64 { - match self.gen.try_next_u64() { - Some(n) => n, - None => panic!("Non-recoverable hardware failure has occured") - } - } - - pub(crate) fn fill_bytes(&mut self, v: &mut [u8]) { - match self.gen.try_fill_bytes(v) { - Ok(_) => {}, - Err(_) => panic!("Non-recoverable hardware failure has occured") - } - } - } -} - -#[cfg(test)] -mod test { - use std::sync::mpsc::channel; - use Rng; - use OsRng; - use std::thread; - - #[test] - fn test_os_rng() { - let mut r = OsRng::new().unwrap(); - - r.next_u32(); - r.next_u64(); - - let mut v = [0u8; 1000]; - r.fill_bytes(&mut v); - } - - #[test] - fn test_os_rng_tasks() { - - let mut txs = vec!(); - for _ in 0..20 { - let (tx, rx) = channel(); - txs.push(tx); - - thread::spawn(move|| { - // wait until all the tasks are ready to go. - rx.recv().unwrap(); - - // deschedule to attempt to interleave things as much - // as possible (XXX: is this a good test?) - let mut r = OsRng::new().unwrap(); - thread::yield_now(); - let mut v = [0u8; 1000]; - - for _ in 0..100 { - r.next_u32(); - thread::yield_now(); - r.next_u64(); - thread::yield_now(); - r.fill_bytes(&mut v); - thread::yield_now(); - } - }); - } - - // start all the tasks - for tx in txs.iter() { - tx.send(()).unwrap(); - } - } -} diff --git a/vendor/rand/src/prng/chacha.rs b/vendor/rand/src/prng/chacha.rs deleted file mode 100644 index a73e8e7..0000000 --- a/vendor/rand/src/prng/chacha.rs +++ /dev/null @@ -1,321 +0,0 @@ -// Copyright 2014 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. - -//! The ChaCha random number generator. - -use core::num::Wrapping as w; -use {Rng, SeedableRng, Rand}; - -#[allow(bad_style)] -type w32 = w<u32>; - -const KEY_WORDS : usize = 8; // 8 words for the 256-bit key -const STATE_WORDS : usize = 16; -const CHACHA_ROUNDS: u32 = 20; // Cryptographically secure from 8 upwards as of this writing - -/// A random number generator that uses the ChaCha20 algorithm [1]. -/// -/// The ChaCha algorithm is widely accepted as suitable for -/// cryptographic purposes, but this implementation has not been -/// verified as such. Prefer a generator like `OsRng` that defers to -/// the operating system for cases that need high security. -/// -/// [1]: D. J. Bernstein, [*ChaCha, a variant of -/// Salsa20*](http://cr.yp.to/chacha.html) -#[derive(Copy, Clone, Debug)] -pub struct ChaChaRng { - buffer: [w32; STATE_WORDS], // Internal buffer of output - state: [w32; STATE_WORDS], // Initial state - index: usize, // Index into state -} - -static EMPTY: ChaChaRng = ChaChaRng { - buffer: [w(0); STATE_WORDS], - state: [w(0); STATE_WORDS], - index: STATE_WORDS -}; - - -macro_rules! quarter_round{ - ($a: expr, $b: expr, $c: expr, $d: expr) => {{ - $a = $a + $b; $d = $d ^ $a; $d = w($d.0.rotate_left(16)); - $c = $c + $d; $b = $b ^ $c; $b = w($b.0.rotate_left(12)); - $a = $a + $b; $d = $d ^ $a; $d = w($d.0.rotate_left( 8)); - $c = $c + $d; $b = $b ^ $c; $b = w($b.0.rotate_left( 7)); - }} -} - -macro_rules! double_round{ - ($x: expr) => {{ - // Column round - quarter_round!($x[ 0], $x[ 4], $x[ 8], $x[12]); - quarter_round!($x[ 1], $x[ 5], $x[ 9], $x[13]); - quarter_round!($x[ 2], $x[ 6], $x[10], $x[14]); - quarter_round!($x[ 3], $x[ 7], $x[11], $x[15]); - // Diagonal round - quarter_round!($x[ 0], $x[ 5], $x[10], $x[15]); - quarter_round!($x[ 1], $x[ 6], $x[11], $x[12]); - quarter_round!($x[ 2], $x[ 7], $x[ 8], $x[13]); - quarter_round!($x[ 3], $x[ 4], $x[ 9], $x[14]); - }} -} - -#[inline] -fn core(output: &mut [w32; STATE_WORDS], input: &[w32; STATE_WORDS]) { - *output = *input; - - for _ in 0..CHACHA_ROUNDS / 2 { - double_round!(output); - } - - for i in 0..STATE_WORDS { - output[i] = output[i] + input[i]; - } -} - -impl ChaChaRng { - - /// Create an ChaCha random number generator using the default - /// fixed key of 8 zero words. - /// - /// # Examples - /// - /// ```rust - /// use rand::{Rng, ChaChaRng}; - /// - /// let mut ra = ChaChaRng::new_unseeded(); - /// println!("{:?}", ra.next_u32()); - /// println!("{:?}", ra.next_u32()); - /// ``` - /// - /// Since this equivalent to a RNG with a fixed seed, repeated executions - /// of an unseeded RNG will produce the same result. This code sample will - /// consistently produce: - /// - /// - 2917185654 - /// - 2419978656 - pub fn new_unseeded() -> ChaChaRng { - let mut rng = EMPTY; - rng.init(&[0; KEY_WORDS]); - rng - } - - /// Sets the internal 128-bit ChaCha counter to - /// a user-provided value. This permits jumping - /// arbitrarily ahead (or backwards) in the pseudorandom stream. - /// - /// Since the nonce words are used to extend the counter to 128 bits, - /// users wishing to obtain the conventional ChaCha pseudorandom stream - /// associated with a particular nonce can call this function with - /// arguments `0, desired_nonce`. - /// - /// # Examples - /// - /// ```rust - /// use rand::{Rng, ChaChaRng}; - /// - /// let mut ra = ChaChaRng::new_unseeded(); - /// ra.set_counter(0u64, 1234567890u64); - /// println!("{:?}", ra.next_u32()); - /// println!("{:?}", ra.next_u32()); - /// ``` - pub fn set_counter(&mut self, counter_low: u64, counter_high: u64) { - self.state[12] = w((counter_low >> 0) as u32); - self.state[13] = w((counter_low >> 32) as u32); - self.state[14] = w((counter_high >> 0) as u32); - self.state[15] = w((counter_high >> 32) as u32); - self.index = STATE_WORDS; // force recomputation - } - - /// Initializes `self.state` with the appropriate key and constants - /// - /// We deviate slightly from the ChaCha specification regarding - /// the nonce, which is used to extend the counter to 128 bits. - /// This is provably as strong as the original cipher, though, - /// since any distinguishing attack on our variant also works - /// against ChaCha with a chosen-nonce. See the XSalsa20 [1] - /// security proof for a more involved example of this. - /// - /// The modified word layout is: - /// ```text - /// constant constant constant constant - /// key key key key - /// key key key key - /// counter counter counter counter - /// ``` - /// [1]: Daniel J. Bernstein. [*Extending the Salsa20 - /// nonce.*](http://cr.yp.to/papers.html#xsalsa) - fn init(&mut self, key: &[u32; KEY_WORDS]) { - self.state[0] = w(0x61707865); - self.state[1] = w(0x3320646E); - self.state[2] = w(0x79622D32); - self.state[3] = w(0x6B206574); - - for i in 0..KEY_WORDS { - self.state[4+i] = w(key[i]); - } - - self.state[12] = w(0); - self.state[13] = w(0); - self.state[14] = w(0); - self.state[15] = w(0); - - self.index = STATE_WORDS; - } - - /// Refill the internal output buffer (`self.buffer`) - fn update(&mut self) { - core(&mut self.buffer, &self.state); - self.index = 0; - // update 128-bit counter - self.state[12] = self.state[12] + w(1); - if self.state[12] != w(0) { return }; - self.state[13] = self.state[13] + w(1); - if self.state[13] != w(0) { return }; - self.state[14] = self.state[14] + w(1); - if self.state[14] != w(0) { return }; - self.state[15] = self.state[15] + w(1); - } -} - -impl Rng for ChaChaRng { - #[inline] - fn next_u32(&mut self) -> u32 { - if self.index == STATE_WORDS { - self.update(); - } - - let value = self.buffer[self.index % STATE_WORDS]; - self.index += 1; - value.0 - } -} - -impl<'a> SeedableRng<&'a [u32]> for ChaChaRng { - - fn reseed(&mut self, seed: &'a [u32]) { - // reset state - self.init(&[0u32; KEY_WORDS]); - // set key in place - let key = &mut self.state[4 .. 4+KEY_WORDS]; - for (k, s) in key.iter_mut().zip(seed.iter()) { - *k = w(*s); - } - } - - /// Create a ChaCha generator from a seed, - /// obtained from a variable-length u32 array. - /// Only up to 8 words are used; if less than 8 - /// words are used, the remaining are set to zero. - fn from_seed(seed: &'a [u32]) -> ChaChaRng { - let mut rng = EMPTY; - rng.reseed(seed); - rng - } -} - -impl Rand for ChaChaRng { - fn rand<R: Rng>(other: &mut R) -> ChaChaRng { - let mut key : [u32; KEY_WORDS] = [0; KEY_WORDS]; - for word in key.iter_mut() { - *word = other.gen(); - } - SeedableRng::from_seed(&key[..]) - } -} - - -#[cfg(test)] -mod test { - use {Rng, SeedableRng}; - use super::ChaChaRng; - - #[test] - fn test_rng_rand_seeded() { - let s = ::test::rng().gen_iter::<u32>().take(8).collect::<Vec<u32>>(); - let mut ra: ChaChaRng = SeedableRng::from_seed(&s[..]); - let mut rb: ChaChaRng = SeedableRng::from_seed(&s[..]); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_seeded() { - let seed : &[_] = &[0,1,2,3,4,5,6,7]; - let mut ra: ChaChaRng = SeedableRng::from_seed(seed); - let mut rb: ChaChaRng = SeedableRng::from_seed(seed); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_reseed() { - let s = ::test::rng().gen_iter::<u32>().take(8).collect::<Vec<u32>>(); - let mut r: ChaChaRng = SeedableRng::from_seed(&s[..]); - let string1: String = r.gen_ascii_chars().take(100).collect(); - - r.reseed(&s); - - let string2: String = r.gen_ascii_chars().take(100).collect(); - assert_eq!(string1, string2); - } - - #[test] - fn test_rng_true_values() { - // Test vectors 1 and 2 from - // http://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04 - let seed : &[_] = &[0u32; 8]; - let mut ra: ChaChaRng = SeedableRng::from_seed(seed); - - let v = (0..16).map(|_| ra.next_u32()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(0xade0b876, 0x903df1a0, 0xe56a5d40, 0x28bd8653, - 0xb819d2bd, 0x1aed8da0, 0xccef36a8, 0xc70d778b, - 0x7c5941da, 0x8d485751, 0x3fe02477, 0x374ad8b8, - 0xf4b8436a, 0x1ca11815, 0x69b687c3, 0x8665eeb2)); - - let v = (0..16).map(|_| ra.next_u32()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(0xbee7079f, 0x7a385155, 0x7c97ba98, 0x0d082d73, - 0xa0290fcb, 0x6965e348, 0x3e53c612, 0xed7aee32, - 0x7621b729, 0x434ee69c, 0xb03371d5, 0xd539d874, - 0x281fed31, 0x45fb0a51, 0x1f0ae1ac, 0x6f4d794b)); - - - let seed : &[_] = &[0,1,2,3,4,5,6,7]; - let mut ra: ChaChaRng = SeedableRng::from_seed(seed); - - // Store the 17*i-th 32-bit word, - // i.e., the i-th word of the i-th 16-word block - let mut v : Vec<u32> = Vec::new(); - for _ in 0..16 { - v.push(ra.next_u32()); - for _ in 0..16 { - ra.next_u32(); - } - } - - assert_eq!(v, - vec!(0xf225c81a, 0x6ab1be57, 0x04d42951, 0x70858036, - 0x49884684, 0x64efec72, 0x4be2d186, 0x3615b384, - 0x11cfa18e, 0xd3c50049, 0x75c775f6, 0x434c6530, - 0x2c5bad8f, 0x898881dc, 0x5f1c86d9, 0xc1f8e7f4)); - } - - #[test] - fn test_rng_clone() { - let seed : &[_] = &[0u32; 8]; - let mut rng: ChaChaRng = SeedableRng::from_seed(seed); - let mut clone = rng.clone(); - for _ in 0..16 { - assert_eq!(rng.next_u64(), clone.next_u64()); - } - } -} diff --git a/vendor/rand/src/prng/isaac.rs b/vendor/rand/src/prng/isaac.rs deleted file mode 100644 index cf5eb67..0000000 --- a/vendor/rand/src/prng/isaac.rs +++ /dev/null @@ -1,328 +0,0 @@ -// 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. - -//! The ISAAC random number generator. - -#![allow(non_camel_case_types)] - -use core::slice; -use core::iter::repeat; -use core::num::Wrapping as w; -use core::fmt; - -use {Rng, SeedableRng, Rand}; - -#[allow(bad_style)] -type w32 = w<u32>; - -const RAND_SIZE_LEN: usize = 8; -const RAND_SIZE: u32 = 1 << RAND_SIZE_LEN; -const RAND_SIZE_USIZE: usize = 1 << RAND_SIZE_LEN; - -/// A random number generator that uses the ISAAC algorithm[1]. -/// -/// The ISAAC algorithm is generally accepted as suitable for -/// cryptographic purposes, but this implementation has not be -/// verified as such. Prefer a generator like `OsRng` that defers to -/// the operating system for cases that need high security. -/// -/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number -/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html) -#[derive(Copy)] -pub struct IsaacRng { - cnt: u32, - rsl: [w32; RAND_SIZE_USIZE], - mem: [w32; RAND_SIZE_USIZE], - a: w32, - b: w32, - c: w32, -} - -static EMPTY: IsaacRng = IsaacRng { - cnt: 0, - rsl: [w(0); RAND_SIZE_USIZE], - mem: [w(0); RAND_SIZE_USIZE], - a: w(0), b: w(0), c: w(0), -}; - -impl IsaacRng { - - /// Create an ISAAC random number generator using the default - /// fixed seed. - pub fn new_unseeded() -> IsaacRng { - let mut rng = EMPTY; - rng.init(false); - rng - } - - /// Initialises `self`. If `use_rsl` is true, then use the current value - /// of `rsl` as a seed, otherwise construct one algorithmically (not - /// randomly). - fn init(&mut self, use_rsl: bool) { - let mut a = w(0x9e3779b9); - let mut b = a; - let mut c = a; - let mut d = a; - let mut e = a; - let mut f = a; - let mut g = a; - let mut h = a; - - macro_rules! mix { - () => {{ - a=a^(b<<11); d=d+a; b=b+c; - b=b^(c>>2); e=e+b; c=c+d; - c=c^(d<<8); f=f+c; d=d+e; - d=d^(e>>16); g=g+d; e=e+f; - e=e^(f<<10); h=h+e; f=f+g; - f=f^(g>>4); a=a+f; g=g+h; - g=g^(h<<8); b=b+g; h=h+a; - h=h^(a>>9); c=c+h; a=a+b; - }} - } - - for _ in 0..4 { - mix!(); - } - - if use_rsl { - macro_rules! memloop { - ($arr:expr) => {{ - for i in (0..RAND_SIZE_USIZE/8).map(|i| i * 8) { - a=a+$arr[i ]; b=b+$arr[i+1]; - c=c+$arr[i+2]; d=d+$arr[i+3]; - e=e+$arr[i+4]; f=f+$arr[i+5]; - g=g+$arr[i+6]; h=h+$arr[i+7]; - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - }} - } - - memloop!(self.rsl); - memloop!(self.mem); - } else { - for i in (0..RAND_SIZE_USIZE/8).map(|i| i * 8) { - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - } - - self.isaac(); - } - - /// Refills the output buffer (`self.rsl`) - #[inline] - fn isaac(&mut self) { - self.c = self.c + w(1); - // abbreviations - let mut a = self.a; - let mut b = self.b + self.c; - - const MIDPOINT: usize = RAND_SIZE_USIZE / 2; - - macro_rules! ind { - ($x:expr) => ( self.mem[($x >> 2usize).0 as usize & (RAND_SIZE_USIZE - 1)] ) - } - - let r = [(0, MIDPOINT), (MIDPOINT, 0)]; - for &(mr_offset, m2_offset) in r.iter() { - - macro_rules! rngstepp { - ($j:expr, $shift:expr) => {{ - let base = $j; - let mix = a << $shift; - - let x = self.mem[base + mr_offset]; - a = (a ^ mix) + self.mem[base + m2_offset]; - let y = ind!(x) + a + b; - self.mem[base + mr_offset] = y; - - b = ind!(y >> RAND_SIZE_LEN) + x; - self.rsl[base + mr_offset] = b; - }} - } - - macro_rules! rngstepn { - ($j:expr, $shift:expr) => {{ - let base = $j; - let mix = a >> $shift; - - let x = self.mem[base + mr_offset]; - a = (a ^ mix) + self.mem[base + m2_offset]; - let y = ind!(x) + a + b; - self.mem[base + mr_offset] = y; - - b = ind!(y >> RAND_SIZE_LEN) + x; - self.rsl[base + mr_offset] = b; - }} - } - - for i in (0..MIDPOINT/4).map(|i| i * 4) { - rngstepp!(i + 0, 13); - rngstepn!(i + 1, 6); - rngstepp!(i + 2, 2); - rngstepn!(i + 3, 16); - } - } - - self.a = a; - self.b = b; - self.cnt = RAND_SIZE; - } -} - -// Cannot be derived because [u32; 256] does not implement Clone -impl Clone for IsaacRng { - fn clone(&self) -> IsaacRng { - *self - } -} - -impl Rng for IsaacRng { - #[inline] - fn next_u32(&mut self) -> u32 { - if self.cnt == 0 { - // make some more numbers - self.isaac(); - } - self.cnt -= 1; - - // self.cnt is at most RAND_SIZE, but that is before the - // subtraction above. We want to index without bounds - // checking, but this could lead to incorrect code if someone - // misrefactors, so we check, sometimes. - // - // (Changes here should be reflected in Isaac64Rng.next_u64.) - debug_assert!(self.cnt < RAND_SIZE); - - // (the % is cheaply telling the optimiser that we're always - // in bounds, without unsafe. NB. this is a power of two, so - // it optimises to a bitwise mask). - self.rsl[(self.cnt % RAND_SIZE) as usize].0 - } -} - -impl<'a> SeedableRng<&'a [u32]> for IsaacRng { - fn reseed(&mut self, seed: &'a [u32]) { - // make the seed into [seed[0], seed[1], ..., seed[seed.len() - // - 1], 0, 0, ...], to fill rng.rsl. - let seed_iter = seed.iter().map(|&x| x).chain(repeat(0u32)); - - for (rsl_elem, seed_elem) in self.rsl.iter_mut().zip(seed_iter) { - *rsl_elem = w(seed_elem); - } - self.cnt = 0; - self.a = w(0); - self.b = w(0); - self.c = w(0); - - self.init(true); - } - - /// Create an ISAAC random number generator with a seed. This can - /// be any length, although the maximum number of elements used is - /// 256 and any more will be silently ignored. A generator - /// constructed with a given seed will generate the same sequence - /// of values as all other generators constructed with that seed. - fn from_seed(seed: &'a [u32]) -> IsaacRng { - let mut rng = EMPTY; - rng.reseed(seed); - rng - } -} - -impl Rand for IsaacRng { - fn rand<R: Rng>(other: &mut R) -> IsaacRng { - let mut ret = EMPTY; - unsafe { - let ptr = ret.rsl.as_mut_ptr() as *mut u8; - - let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE_USIZE * 4); - other.fill_bytes(slice); - } - ret.cnt = 0; - ret.a = w(0); - ret.b = w(0); - ret.c = w(0); - - ret.init(true); - return ret; - } -} - -impl fmt::Debug for IsaacRng { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "IsaacRng {{}}") - } -} - -#[cfg(test)] -mod test { - use {Rng, SeedableRng}; - use super::IsaacRng; - - #[test] - fn test_rng_32_rand_seeded() { - let s = ::test::rng().gen_iter::<u32>().take(256).collect::<Vec<u32>>(); - let mut ra: IsaacRng = SeedableRng::from_seed(&s[..]); - let mut rb: IsaacRng = SeedableRng::from_seed(&s[..]); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_32_seeded() { - let seed: &[_] = &[1, 23, 456, 7890, 12345]; - let mut ra: IsaacRng = SeedableRng::from_seed(seed); - let mut rb: IsaacRng = SeedableRng::from_seed(seed); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_32_reseed() { - let s = ::test::rng().gen_iter::<u32>().take(256).collect::<Vec<u32>>(); - let mut r: IsaacRng = SeedableRng::from_seed(&s[..]); - let string1: String = r.gen_ascii_chars().take(100).collect(); - - r.reseed(&s[..]); - - let string2: String = r.gen_ascii_chars().take(100).collect(); - assert_eq!(string1, string2); - } - - #[test] - fn test_rng_32_true_values() { - let seed: &[_] = &[1, 23, 456, 7890, 12345]; - let mut ra: IsaacRng = SeedableRng::from_seed(seed); - // Regression test that isaac is actually using the above vector - let v = (0..10).map(|_| ra.next_u32()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(2558573138, 873787463, 263499565, 2103644246, 3595684709, - 4203127393, 264982119, 2765226902, 2737944514, 3900253796)); - - let seed: &[_] = &[12345, 67890, 54321, 9876]; - let mut rb: IsaacRng = SeedableRng::from_seed(seed); - // skip forward to the 10000th number - for _ in 0..10000 { rb.next_u32(); } - - let v = (0..10).map(|_| rb.next_u32()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(3676831399, 3183332890, 2834741178, 3854698763, 2717568474, - 1576568959, 3507990155, 179069555, 141456972, 2478885421)); - } -} diff --git a/vendor/rand/src/prng/isaac64.rs b/vendor/rand/src/prng/isaac64.rs deleted file mode 100644 index b98e3fe..0000000 --- a/vendor/rand/src/prng/isaac64.rs +++ /dev/null @@ -1,340 +0,0 @@ -// 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. - -//! The ISAAC-64 random number generator. - -use core::slice; -use core::iter::repeat; -use core::num::Wrapping as w; -use core::fmt; - -use {Rng, SeedableRng, Rand}; - -#[allow(bad_style)] -type w64 = w<u64>; - -const RAND_SIZE_64_LEN: usize = 8; -const RAND_SIZE_64: usize = 1 << RAND_SIZE_64_LEN; - -/// A random number generator that uses ISAAC-64[1], the 64-bit -/// variant of the ISAAC algorithm. -/// -/// The ISAAC algorithm is generally accepted as suitable for -/// cryptographic purposes, but this implementation has not be -/// verified as such. Prefer a generator like `OsRng` that defers to -/// the operating system for cases that need high security. -/// -/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number -/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html) -#[derive(Copy)] -pub struct Isaac64Rng { - cnt: usize, - rsl: [w64; RAND_SIZE_64], - mem: [w64; RAND_SIZE_64], - a: w64, - b: w64, - c: w64, -} - -static EMPTY_64: Isaac64Rng = Isaac64Rng { - cnt: 0, - rsl: [w(0); RAND_SIZE_64], - mem: [w(0); RAND_SIZE_64], - a: w(0), b: w(0), c: w(0), -}; - -impl Isaac64Rng { - /// Create a 64-bit ISAAC random number generator using the - /// default fixed seed. - pub fn new_unseeded() -> Isaac64Rng { - let mut rng = EMPTY_64; - rng.init(false); - rng - } - - /// Initialises `self`. If `use_rsl` is true, then use the current value - /// of `rsl` as a seed, otherwise construct one algorithmically (not - /// randomly). - fn init(&mut self, use_rsl: bool) { - macro_rules! init { - ($var:ident) => ( - let mut $var = w(0x9e3779b97f4a7c13); - ) - } - init!(a); init!(b); init!(c); init!(d); - init!(e); init!(f); init!(g); init!(h); - - macro_rules! mix { - () => {{ - a=a-e; f=f^(h>>9); h=h+a; - b=b-f; g=g^(a<<9); a=a+b; - c=c-g; h=h^(b>>23); b=b+c; - d=d-h; a=a^(c<<15); c=c+d; - e=e-a; b=b^(d>>14); d=d+e; - f=f-b; c=c^(e<<20); e=e+f; - g=g-c; d=d^(f>>17); f=f+g; - h=h-d; e=e^(g<<14); g=g+h; - }} - } - - for _ in 0..4 { - mix!(); - } - - if use_rsl { - macro_rules! memloop { - ($arr:expr) => {{ - for i in (0..RAND_SIZE_64 / 8).map(|i| i * 8) { - a=a+$arr[i ]; b=b+$arr[i+1]; - c=c+$arr[i+2]; d=d+$arr[i+3]; - e=e+$arr[i+4]; f=f+$arr[i+5]; - g=g+$arr[i+6]; h=h+$arr[i+7]; - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - }} - } - - memloop!(self.rsl); - memloop!(self.mem); - } else { - for i in (0..RAND_SIZE_64 / 8).map(|i| i * 8) { - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - } - - self.isaac64(); - } - - /// Refills the output buffer (`self.rsl`) - fn isaac64(&mut self) { - self.c = self.c + w(1); - // abbreviations - let mut a = self.a; - let mut b = self.b + self.c; - const MIDPOINT: usize = RAND_SIZE_64 / 2; - const MP_VEC: [(usize, usize); 2] = [(0,MIDPOINT), (MIDPOINT, 0)]; - macro_rules! ind { - ($x:expr) => { - *self.mem.get_unchecked((($x >> 3usize).0 as usize) & (RAND_SIZE_64 - 1)) - } - } - - for &(mr_offset, m2_offset) in MP_VEC.iter() { - for base in (0..MIDPOINT / 4).map(|i| i * 4) { - - macro_rules! rngstepp { - ($j:expr, $shift:expr) => {{ - let base = base + $j; - let mix = a ^ (a << $shift); - let mix = if $j == 0 {!mix} else {mix}; - - unsafe { - let x = *self.mem.get_unchecked(base + mr_offset); - a = mix + *self.mem.get_unchecked(base + m2_offset); - let y = ind!(x) + a + b; - *self.mem.get_unchecked_mut(base + mr_offset) = y; - - b = ind!(y >> RAND_SIZE_64_LEN) + x; - *self.rsl.get_unchecked_mut(base + mr_offset) = b; - } - }} - } - - macro_rules! rngstepn { - ($j:expr, $shift:expr) => {{ - let base = base + $j; - let mix = a ^ (a >> $shift); - let mix = if $j == 0 {!mix} else {mix}; - - unsafe { - let x = *self.mem.get_unchecked(base + mr_offset); - a = mix + *self.mem.get_unchecked(base + m2_offset); - let y = ind!(x) + a + b; - *self.mem.get_unchecked_mut(base + mr_offset) = y; - - b = ind!(y >> RAND_SIZE_64_LEN) + x; - *self.rsl.get_unchecked_mut(base + mr_offset) = b; - } - }} - } - - rngstepp!(0, 21); - rngstepn!(1, 5); - rngstepp!(2, 12); - rngstepn!(3, 33); - } - } - - self.a = a; - self.b = b; - self.cnt = RAND_SIZE_64; - } -} - -// Cannot be derived because [u32; 256] does not implement Clone -impl Clone for Isaac64Rng { - fn clone(&self) -> Isaac64Rng { - *self - } -} - -impl Rng for Isaac64Rng { - #[inline] - fn next_u32(&mut self) -> u32 { - self.next_u64() as u32 - } - - #[inline] - fn next_u64(&mut self) -> u64 { - if self.cnt == 0 { - // make some more numbers - self.isaac64(); - } - self.cnt -= 1; - - // See corresponding location in IsaacRng.next_u32 for - // explanation. - debug_assert!(self.cnt < RAND_SIZE_64); - self.rsl[(self.cnt % RAND_SIZE_64) as usize].0 - } -} - -impl<'a> SeedableRng<&'a [u64]> for Isaac64Rng { - fn reseed(&mut self, seed: &'a [u64]) { - // make the seed into [seed[0], seed[1], ..., seed[seed.len() - // - 1], 0, 0, ...], to fill rng.rsl. - let seed_iter = seed.iter().map(|&x| x).chain(repeat(0u64)); - - for (rsl_elem, seed_elem) in self.rsl.iter_mut().zip(seed_iter) { - *rsl_elem = w(seed_elem); - } - self.cnt = 0; - self.a = w(0); - self.b = w(0); - self.c = w(0); - - self.init(true); - } - - /// Create an ISAAC random number generator with a seed. This can - /// be any length, although the maximum number of elements used is - /// 256 and any more will be silently ignored. A generator - /// constructed with a given seed will generate the same sequence - /// of values as all other generators constructed with that seed. - fn from_seed(seed: &'a [u64]) -> Isaac64Rng { - let mut rng = EMPTY_64; - rng.reseed(seed); - rng - } -} - -impl Rand for Isaac64Rng { - fn rand<R: Rng>(other: &mut R) -> Isaac64Rng { - let mut ret = EMPTY_64; - unsafe { - let ptr = ret.rsl.as_mut_ptr() as *mut u8; - - let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE_64 * 8); - other.fill_bytes(slice); - } - ret.cnt = 0; - ret.a = w(0); - ret.b = w(0); - ret.c = w(0); - - ret.init(true); - return ret; - } -} - -impl fmt::Debug for Isaac64Rng { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "Isaac64Rng {{}}") - } -} - -#[cfg(test)] -mod test { - use {Rng, SeedableRng}; - use super::Isaac64Rng; - - #[test] - fn test_rng_64_rand_seeded() { - let s = ::test::rng().gen_iter::<u64>().take(256).collect::<Vec<u64>>(); - let mut ra: Isaac64Rng = SeedableRng::from_seed(&s[..]); - let mut rb: Isaac64Rng = SeedableRng::from_seed(&s[..]); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_64_seeded() { - let seed: &[_] = &[1, 23, 456, 7890, 12345]; - let mut ra: Isaac64Rng = SeedableRng::from_seed(seed); - let mut rb: Isaac64Rng = SeedableRng::from_seed(seed); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_64_reseed() { - let s = ::test::rng().gen_iter::<u64>().take(256).collect::<Vec<u64>>(); - let mut r: Isaac64Rng = SeedableRng::from_seed(&s[..]); - let string1: String = r.gen_ascii_chars().take(100).collect(); - - r.reseed(&s[..]); - - let string2: String = r.gen_ascii_chars().take(100).collect(); - assert_eq!(string1, string2); - } - - #[test] - fn test_rng_64_true_values() { - let seed: &[_] = &[1, 23, 456, 7890, 12345]; - let mut ra: Isaac64Rng = SeedableRng::from_seed(seed); - // Regression test that isaac is actually using the above vector - let v = (0..10).map(|_| ra.next_u64()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(547121783600835980, 14377643087320773276, 17351601304698403469, - 1238879483818134882, 11952566807690396487, 13970131091560099343, - 4469761996653280935, 15552757044682284409, 6860251611068737823, - 13722198873481261842)); - - let seed: &[_] = &[12345, 67890, 54321, 9876]; - let mut rb: Isaac64Rng = SeedableRng::from_seed(seed); - // skip forward to the 10000th number - for _ in 0..10000 { rb.next_u64(); } - - let v = (0..10).map(|_| rb.next_u64()).collect::<Vec<_>>(); - assert_eq!(v, - vec!(18143823860592706164, 8491801882678285927, 2699425367717515619, - 17196852593171130876, 2606123525235546165, 15790932315217671084, - 596345674630742204, 9947027391921273664, 11788097613744130851, - 10391409374914919106)); - } - - #[test] - fn test_rng_clone() { - let seed: &[_] = &[1, 23, 456, 7890, 12345]; - let mut rng: Isaac64Rng = SeedableRng::from_seed(seed); - let mut clone = rng.clone(); - for _ in 0..16 { - assert_eq!(rng.next_u64(), clone.next_u64()); - } - } -} diff --git a/vendor/rand/src/prng/mod.rs b/vendor/rand/src/prng/mod.rs deleted file mode 100644 index ed3e018..0000000 --- a/vendor/rand/src/prng/mod.rs +++ /dev/null @@ -1,51 +0,0 @@ -// Copyright 2017 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. - -//! Pseudo random number generators are algorithms to produce *apparently -//! random* numbers deterministically, and usually fairly quickly. -//! -//! So long as the algorithm is computationally secure, is initialised with -//! sufficient entropy (i.e. unknown by an attacker), and its internal state is -//! also protected (unknown to an attacker), the output will also be -//! *computationally secure*. Computationally Secure Pseudo Random Number -//! Generators (CSPRNGs) are thus suitable sources of random numbers for -//! cryptography. There are a couple of gotchas here, however. First, the seed -//! used for initialisation must be unknown. Usually this should be provided by -//! the operating system and should usually be secure, however this may not -//! always be the case (especially soon after startup). Second, user-space -//! memory may be vulnerable, for example when written to swap space, and after -//! forking a child process should reinitialise any user-space PRNGs. For this -//! reason it may be preferable to source random numbers directly from the OS -//! for cryptographic applications. -//! -//! PRNGs are also widely used for non-cryptographic uses: randomised -//! algorithms, simulations, games. In these applications it is usually not -//! important for numbers to be cryptographically *unguessable*, but even -//! distribution and independence from other samples (from the point of view -//! of someone unaware of the algorithm used, at least) may still be important. -//! Good PRNGs should satisfy these properties, but do not take them for -//! granted; Wikipedia's article on -//! [Pseudorandom number generators](https://en.wikipedia.org/wiki/Pseudorandom_number_generator) -//! provides some background on this topic. -//! -//! Care should be taken when seeding (initialising) PRNGs. Some PRNGs have -//! short periods for some seeds. If one PRNG is seeded from another using the -//! same algorithm, it is possible that both will yield the same sequence of -//! values (with some lag). - -mod chacha; -mod isaac; -mod isaac64; -mod xorshift; - -pub use self::chacha::ChaChaRng; -pub use self::isaac::IsaacRng; -pub use self::isaac64::Isaac64Rng; -pub use self::xorshift::XorShiftRng; diff --git a/vendor/rand/src/prng/xorshift.rs b/vendor/rand/src/prng/xorshift.rs deleted file mode 100644 index dd367e9..0000000 --- a/vendor/rand/src/prng/xorshift.rs +++ /dev/null @@ -1,101 +0,0 @@ -// Copyright 2017 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. - -//! Xorshift generators - -use core::num::Wrapping as w; -use {Rng, SeedableRng, Rand}; - -/// An Xorshift[1] random number -/// generator. -/// -/// The Xorshift algorithm is not suitable for cryptographic purposes -/// but is very fast. If you do not know for sure that it fits your -/// requirements, use a more secure one such as `IsaacRng` or `OsRng`. -/// -/// [1]: Marsaglia, George (July 2003). ["Xorshift -/// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of -/// Statistical Software*. Vol. 8 (Issue 14). -#[allow(missing_copy_implementations)] -#[derive(Clone, Debug)] -pub struct XorShiftRng { - x: w<u32>, - y: w<u32>, - z: w<u32>, - w: w<u32>, -} - -impl XorShiftRng { - /// Creates a new XorShiftRng instance which is not seeded. - /// - /// The initial values of this RNG are constants, so all generators created - /// by this function will yield the same stream of random numbers. It is - /// highly recommended that this is created through `SeedableRng` instead of - /// this function - pub fn new_unseeded() -> XorShiftRng { - XorShiftRng { - x: w(0x193a6754), - y: w(0xa8a7d469), - z: w(0x97830e05), - w: w(0x113ba7bb), - } - } -} - -impl Rng for XorShiftRng { - #[inline] - fn next_u32(&mut self) -> u32 { - let x = self.x; - let t = x ^ (x << 11); - self.x = self.y; - self.y = self.z; - self.z = self.w; - let w_ = self.w; - self.w = w_ ^ (w_ >> 19) ^ (t ^ (t >> 8)); - self.w.0 - } -} - -impl SeedableRng<[u32; 4]> for XorShiftRng { - /// Reseed an XorShiftRng. This will panic if `seed` is entirely 0. - fn reseed(&mut self, seed: [u32; 4]) { - assert!(!seed.iter().all(|&x| x == 0), - "XorShiftRng.reseed called with an all zero seed."); - - self.x = w(seed[0]); - self.y = w(seed[1]); - self.z = w(seed[2]); - self.w = w(seed[3]); - } - - /// Create a new XorShiftRng. This will panic if `seed` is entirely 0. - fn from_seed(seed: [u32; 4]) -> XorShiftRng { - assert!(!seed.iter().all(|&x| x == 0), - "XorShiftRng::from_seed called with an all zero seed."); - - XorShiftRng { - x: w(seed[0]), - y: w(seed[1]), - z: w(seed[2]), - w: w(seed[3]), - } - } -} - -impl Rand for XorShiftRng { - fn rand<R: Rng>(rng: &mut R) -> XorShiftRng { - let mut tuple: (u32, u32, u32, u32) = rng.gen(); - while tuple == (0, 0, 0, 0) { - tuple = rng.gen(); - } - let (x, y, z, w_) = tuple; - XorShiftRng { x: w(x), y: w(y), z: w(z), w: w(w_) } - } -} diff --git a/vendor/rand/src/rand_impls.rs b/vendor/rand/src/rand_impls.rs deleted file mode 100644 index a865bb6..0000000 --- a/vendor/rand/src/rand_impls.rs +++ /dev/null @@ -1,299 +0,0 @@ -// Copyright 2013-2014 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. - -//! The implementations of `Rand` for the built-in types. - -use core::{char, mem}; - -use {Rand,Rng}; - -impl Rand for isize { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> isize { - if mem::size_of::<isize>() == 4 { - rng.gen::<i32>() as isize - } else { - rng.gen::<i64>() as isize - } - } -} - -impl Rand for i8 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i8 { - rng.next_u32() as i8 - } -} - -impl Rand for i16 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i16 { - rng.next_u32() as i16 - } -} - -impl Rand for i32 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i32 { - rng.next_u32() as i32 - } -} - -impl Rand for i64 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i64 { - rng.next_u64() as i64 - } -} - -#[cfg(feature = "i128_support")] -impl Rand for i128 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i128 { - rng.gen::<u128>() as i128 - } -} - -impl Rand for usize { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> usize { - if mem::size_of::<usize>() == 4 { - rng.gen::<u32>() as usize - } else { - rng.gen::<u64>() as usize - } - } -} - -impl Rand for u8 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u8 { - rng.next_u32() as u8 - } -} - -impl Rand for u16 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u16 { - rng.next_u32() as u16 - } -} - -impl Rand for u32 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u32 { - rng.next_u32() - } -} - -impl Rand for u64 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u64 { - rng.next_u64() - } -} - -#[cfg(feature = "i128_support")] -impl Rand for u128 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u128 { - ((rng.next_u64() as u128) << 64) | (rng.next_u64() as u128) - } -} - - -macro_rules! float_impls { - ($mod_name:ident, $ty:ty, $mantissa_bits:expr, $method_name:ident) => { - mod $mod_name { - use {Rand, Rng, Open01, Closed01}; - - const SCALE: $ty = (1u64 << $mantissa_bits) as $ty; - - impl Rand for $ty { - /// Generate a floating point number in the half-open - /// interval `[0,1)`. - /// - /// See `Closed01` for the closed interval `[0,1]`, - /// and `Open01` for the open interval `(0,1)`. - #[inline] - fn rand<R: Rng>(rng: &mut R) -> $ty { - rng.$method_name() - } - } - impl Rand for Open01<$ty> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Open01<$ty> { - // add a small amount (specifically 2 bits below - // the precision of f64/f32 at 1.0), so that small - // numbers are larger than 0, but large numbers - // aren't pushed to/above 1. - Open01(rng.$method_name() + 0.25 / SCALE) - } - } - impl Rand for Closed01<$ty> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Closed01<$ty> { - // rescale so that 1.0 - epsilon becomes 1.0 - // precisely. - Closed01(rng.$method_name() * SCALE / (SCALE - 1.0)) - } - } - } - } -} -float_impls! { f64_rand_impls, f64, 53, next_f64 } -float_impls! { f32_rand_impls, f32, 24, next_f32 } - -impl Rand for char { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> char { - // a char is 21 bits - const CHAR_MASK: u32 = 0x001f_ffff; - loop { - // Rejection sampling. About 0.2% of numbers with at most - // 21-bits are invalid codepoints (surrogates), so this - // will succeed first go almost every time. - match char::from_u32(rng.next_u32() & CHAR_MASK) { - Some(c) => return c, - None => {} - } - } - } -} - -impl Rand for bool { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> bool { - rng.gen::<u8>() & 1 == 1 - } -} - -macro_rules! tuple_impl { - // use variables to indicate the arity of the tuple - ($($tyvar:ident),* ) => { - // the trailing commas are for the 1 tuple - impl< - $( $tyvar : Rand ),* - > Rand for ( $( $tyvar ),* , ) { - - #[inline] - fn rand<R: Rng>(_rng: &mut R) -> ( $( $tyvar ),* , ) { - ( - // use the $tyvar's to get the appropriate number of - // repeats (they're not actually needed) - $( - _rng.gen::<$tyvar>() - ),* - , - ) - } - } - } -} - -impl Rand for () { - #[inline] - fn rand<R: Rng>(_: &mut R) -> () { () } -} -tuple_impl!{A} -tuple_impl!{A, B} -tuple_impl!{A, B, C} -tuple_impl!{A, B, C, D} -tuple_impl!{A, B, C, D, E} -tuple_impl!{A, B, C, D, E, F} -tuple_impl!{A, B, C, D, E, F, G} -tuple_impl!{A, B, C, D, E, F, G, H} -tuple_impl!{A, B, C, D, E, F, G, H, I} -tuple_impl!{A, B, C, D, E, F, G, H, I, J} -tuple_impl!{A, B, C, D, E, F, G, H, I, J, K} -tuple_impl!{A, B, C, D, E, F, G, H, I, J, K, L} - -macro_rules! array_impl { - {$n:expr, $t:ident, $($ts:ident,)*} => { - array_impl!{($n - 1), $($ts,)*} - - impl<T> Rand for [T; $n] where T: Rand { - #[inline] - fn rand<R: Rng>(_rng: &mut R) -> [T; $n] { - [_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*] - } - } - }; - {$n:expr,} => { - impl<T> Rand for [T; $n] { - fn rand<R: Rng>(_rng: &mut R) -> [T; $n] { [] } - } - }; -} - -array_impl!{32, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T,} - -impl<T:Rand> Rand for Option<T> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Option<T> { - if rng.gen() { - Some(rng.gen()) - } else { - None - } - } -} - -#[cfg(test)] -mod tests { - use {Rng, thread_rng, Open01, Closed01}; - - struct ConstantRng(u64); - impl Rng for ConstantRng { - fn next_u32(&mut self) -> u32 { - let ConstantRng(v) = *self; - v as u32 - } - fn next_u64(&mut self) -> u64 { - let ConstantRng(v) = *self; - v - } - } - - #[test] - fn floating_point_edge_cases() { - // the test for exact equality is correct here. - assert!(ConstantRng(0xffff_ffff).gen::<f32>() != 1.0); - assert!(ConstantRng(0xffff_ffff_ffff_ffff).gen::<f64>() != 1.0); - } - - #[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 = thread_rng(); - for _ in 0..1_000 { - // strict inequalities - let Open01(f) = rng.gen::<Open01<f64>>(); - assert!(0.0 < f && f < 1.0); - - let Open01(f) = rng.gen::<Open01<f32>>(); - assert!(0.0 < f && f < 1.0); - } - } - - #[test] - fn rand_closed() { - let mut rng = thread_rng(); - for _ in 0..1_000 { - // strict inequalities - let Closed01(f) = rng.gen::<Closed01<f64>>(); - assert!(0.0 <= f && f <= 1.0); - - let Closed01(f) = rng.gen::<Closed01<f32>>(); - assert!(0.0 <= f && f <= 1.0); - } - } -} diff --git a/vendor/rand/src/read.rs b/vendor/rand/src/read.rs deleted file mode 100644 index c7351b7..0000000 --- a/vendor/rand/src/read.rs +++ /dev/null @@ -1,123 +0,0 @@ -// 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. - -//! A wrapper around any Read to treat it as an RNG. - -use std::io::{self, Read}; -use std::mem; -use Rng; - -/// An RNG that reads random bytes straight from a `Read`. This will -/// work best with an infinite reader, but this is not required. -/// -/// # Panics -/// -/// It will panic if it there is insufficient data to fulfill a request. -/// -/// # Example -/// -/// ```rust -/// use rand::{read, Rng}; -/// -/// let data = vec![1, 2, 3, 4, 5, 6, 7, 8]; -/// let mut rng = read::ReadRng::new(&data[..]); -/// println!("{:x}", rng.gen::<u32>()); -/// ``` -#[derive(Debug)] -pub struct ReadRng<R> { - reader: R -} - -impl<R: Read> ReadRng<R> { - /// Create a new `ReadRng` from a `Read`. - pub fn new(r: R) -> ReadRng<R> { - ReadRng { - reader: r - } - } -} - -impl<R: Read> Rng for ReadRng<R> { - fn next_u32(&mut self) -> u32 { - // This is designed for speed: reading a LE integer on a LE - // platform just involves blitting the bytes into the memory - // of the u32, similarly for BE on BE; avoiding byteswapping. - let mut buf = [0; 4]; - fill(&mut self.reader, &mut buf).unwrap(); - unsafe { *(buf.as_ptr() as *const u32) } - } - fn next_u64(&mut self) -> u64 { - // see above for explanation. - let mut buf = [0; 8]; - fill(&mut self.reader, &mut buf).unwrap(); - unsafe { *(buf.as_ptr() as *const u64) } - } - fn fill_bytes(&mut self, v: &mut [u8]) { - if v.len() == 0 { return } - fill(&mut self.reader, v).unwrap(); - } -} - -fn fill(r: &mut Read, mut buf: &mut [u8]) -> io::Result<()> { - while buf.len() > 0 { - match try!(r.read(buf)) { - 0 => return Err(io::Error::new(io::ErrorKind::Other, - "end of file reached")), - n => buf = &mut mem::replace(&mut buf, &mut [])[n..], - } - } - Ok(()) -} - -#[cfg(test)] -mod test { - use super::ReadRng; - use Rng; - - #[test] - fn test_reader_rng_u64() { - // transmute from the target to avoid endianness concerns. - let v = vec![0u8, 0, 0, 0, 0, 0, 0, 1, - 0 , 0, 0, 0, 0, 0, 0, 2, - 0, 0, 0, 0, 0, 0, 0, 3]; - let mut rng = ReadRng::new(&v[..]); - - assert_eq!(rng.next_u64(), 1_u64.to_be()); - assert_eq!(rng.next_u64(), 2_u64.to_be()); - assert_eq!(rng.next_u64(), 3_u64.to_be()); - } - #[test] - fn test_reader_rng_u32() { - let v = vec![0u8, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 3]; - let mut rng = ReadRng::new(&v[..]); - - assert_eq!(rng.next_u32(), 1_u32.to_be()); - assert_eq!(rng.next_u32(), 2_u32.to_be()); - assert_eq!(rng.next_u32(), 3_u32.to_be()); - } - #[test] - fn test_reader_rng_fill_bytes() { - let v = [1u8, 2, 3, 4, 5, 6, 7, 8]; - let mut w = [0u8; 8]; - - let mut rng = ReadRng::new(&v[..]); - rng.fill_bytes(&mut w); - - assert!(v == w); - } - - #[test] - #[should_panic] - fn test_reader_rng_insufficient_bytes() { - let mut rng = ReadRng::new(&[][..]); - let mut v = [0u8; 3]; - rng.fill_bytes(&mut v); - } -} diff --git a/vendor/rand/src/reseeding.rs b/vendor/rand/src/reseeding.rs deleted file mode 100644 index 1f24e20..0000000 --- a/vendor/rand/src/reseeding.rs +++ /dev/null @@ -1,229 +0,0 @@ -// 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. - -//! A wrapper around another RNG that reseeds it after it -//! generates a certain number of random bytes. - -use core::default::Default; - -use {Rng, SeedableRng}; - -/// How many bytes of entropy the underling RNG is allowed to generate -/// before it is reseeded -const DEFAULT_GENERATION_THRESHOLD: u64 = 32 * 1024; - -/// A wrapper around any RNG which reseeds the underlying RNG after it -/// has generated a certain number of random bytes. -#[derive(Debug)] -pub struct ReseedingRng<R, Rsdr> { - rng: R, - generation_threshold: u64, - bytes_generated: u64, - /// Controls the behaviour when reseeding the RNG. - pub reseeder: Rsdr, -} - -impl<R: Rng, Rsdr: Reseeder<R>> ReseedingRng<R, Rsdr> { - /// Create a new `ReseedingRng` with the given parameters. - /// - /// # Arguments - /// - /// * `rng`: the random number generator to use. - /// * `generation_threshold`: the number of bytes of entropy at which to reseed the RNG. - /// * `reseeder`: the reseeding object to use. - pub fn new(rng: R, generation_threshold: u64, reseeder: Rsdr) -> ReseedingRng<R,Rsdr> { - ReseedingRng { - rng: rng, - generation_threshold: generation_threshold, - bytes_generated: 0, - reseeder: reseeder - } - } - - /// Reseed the internal RNG if the number of bytes that have been - /// generated exceed the threshold. - pub fn reseed_if_necessary(&mut self) { - if self.bytes_generated >= self.generation_threshold { - self.reseeder.reseed(&mut self.rng); - self.bytes_generated = 0; - } - } -} - - -impl<R: Rng, Rsdr: Reseeder<R>> Rng for ReseedingRng<R, Rsdr> { - fn next_u32(&mut self) -> u32 { - self.reseed_if_necessary(); - self.bytes_generated += 4; - self.rng.next_u32() - } - - fn next_u64(&mut self) -> u64 { - self.reseed_if_necessary(); - self.bytes_generated += 8; - self.rng.next_u64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - self.reseed_if_necessary(); - self.bytes_generated += dest.len() as u64; - self.rng.fill_bytes(dest) - } -} - -impl<S, R: SeedableRng<S>, Rsdr: Reseeder<R> + Default> - SeedableRng<(Rsdr, S)> for ReseedingRng<R, Rsdr> { - fn reseed(&mut self, (rsdr, seed): (Rsdr, S)) { - self.rng.reseed(seed); - self.reseeder = rsdr; - self.bytes_generated = 0; - } - - /// Create a new `ReseedingRng` from the given reseeder and - /// seed. This uses a default value for `generation_threshold`. - fn from_seed((rsdr, seed): (Rsdr, S)) -> ReseedingRng<R, Rsdr> { - ReseedingRng { - rng: SeedableRng::from_seed(seed), - generation_threshold: DEFAULT_GENERATION_THRESHOLD, - bytes_generated: 0, - reseeder: rsdr - } - } -} - -/// Something that can be used to reseed an RNG via `ReseedingRng`. -/// -/// # Example -/// -/// ```rust -/// use rand::{Rng, SeedableRng, StdRng}; -/// use rand::reseeding::{Reseeder, ReseedingRng}; -/// -/// struct TickTockReseeder { tick: bool } -/// impl Reseeder<StdRng> for TickTockReseeder { -/// fn reseed(&mut self, rng: &mut StdRng) { -/// let val = if self.tick {0} else {1}; -/// rng.reseed(&[val]); -/// self.tick = !self.tick; -/// } -/// } -/// fn main() { -/// let rsdr = TickTockReseeder { tick: true }; -/// -/// let inner = StdRng::new().unwrap(); -/// let mut rng = ReseedingRng::new(inner, 10, rsdr); -/// -/// // this will repeat, because it gets reseeded very regularly. -/// let s: String = rng.gen_ascii_chars().take(100).collect(); -/// println!("{}", s); -/// } -/// -/// ``` -pub trait Reseeder<R> { - /// Reseed the given RNG. - fn reseed(&mut self, rng: &mut R); -} - -/// Reseed an RNG using a `Default` instance. This reseeds by -/// replacing the RNG with the result of a `Default::default` call. -#[derive(Clone, Copy, Debug)] -pub struct ReseedWithDefault; - -impl<R: Rng + Default> Reseeder<R> for ReseedWithDefault { - fn reseed(&mut self, rng: &mut R) { - *rng = Default::default(); - } -} -impl Default for ReseedWithDefault { - fn default() -> ReseedWithDefault { ReseedWithDefault } -} - -#[cfg(test)] -mod test { - use std::default::Default; - use std::iter::repeat; - use super::{ReseedingRng, ReseedWithDefault}; - use {SeedableRng, Rng}; - - struct Counter { - i: u32 - } - - impl Rng for Counter { - fn next_u32(&mut self) -> u32 { - self.i += 1; - // very random - self.i - 1 - } - } - impl Default for Counter { - fn default() -> Counter { - Counter { i: 0 } - } - } - impl SeedableRng<u32> for Counter { - fn reseed(&mut self, seed: u32) { - self.i = seed; - } - fn from_seed(seed: u32) -> Counter { - Counter { i: seed } - } - } - type MyRng = ReseedingRng<Counter, ReseedWithDefault>; - - #[test] - fn test_reseeding() { - let mut rs = ReseedingRng::new(Counter {i:0}, 400, ReseedWithDefault); - - let mut i = 0; - for _ in 0..1000 { - assert_eq!(rs.next_u32(), i % 100); - i += 1; - } - } - - #[test] - fn test_rng_seeded() { - let mut ra: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2)); - let mut rb: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2)); - assert!(::test::iter_eq(ra.gen_ascii_chars().take(100), - rb.gen_ascii_chars().take(100))); - } - - #[test] - fn test_rng_reseed() { - let mut r: MyRng = SeedableRng::from_seed((ReseedWithDefault, 3)); - let string1: String = r.gen_ascii_chars().take(100).collect(); - - r.reseed((ReseedWithDefault, 3)); - - let string2: String = r.gen_ascii_chars().take(100).collect(); - assert_eq!(string1, string2); - } - - const FILL_BYTES_V_LEN: usize = 13579; - #[test] - fn test_rng_fill_bytes() { - let mut v = repeat(0u8).take(FILL_BYTES_V_LEN).collect::<Vec<_>>(); - ::test::rng().fill_bytes(&mut v); - - // Sanity test: if we've gotten here, `fill_bytes` has not infinitely - // recursed. - assert_eq!(v.len(), FILL_BYTES_V_LEN); - - // To test that `fill_bytes` actually did something, check that the - // average of `v` is not 0. - let mut sum = 0.0; - for &x in v.iter() { - sum += x as f64; - } - assert!(sum / v.len() as f64 != 0.0); - } -} diff --git a/vendor/rand/src/seq.rs b/vendor/rand/src/seq.rs deleted file mode 100644 index a7889fe..0000000 --- a/vendor/rand/src/seq.rs +++ /dev/null @@ -1,337 +0,0 @@ -// Copyright 2017 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. - -//! Functions for randomly accessing and sampling sequences. - -use super::Rng; - -// This crate is only enabled when either std or alloc is available. -// BTreeMap is not as fast in tests, but better than nothing. -#[cfg(feature="std")] use std::collections::HashMap; -#[cfg(not(feature="std"))] use alloc::btree_map::BTreeMap; - -#[cfg(not(feature="std"))] use alloc::Vec; - -/// Randomly sample `amount` elements from a finite iterator. -/// -/// The following can be returned: -/// - `Ok`: `Vec` of `amount` non-repeating randomly sampled elements. The order is not random. -/// - `Err`: `Vec` of all the elements from `iterable` in sequential order. This happens when the -/// length of `iterable` was less than `amount`. This is considered an error since exactly -/// `amount` elements is typically expected. -/// -/// This implementation uses `O(len(iterable))` time and `O(amount)` memory. -/// -/// # Example -/// -/// ```rust -/// use rand::{thread_rng, seq}; -/// -/// let mut rng = thread_rng(); -/// let sample = seq::sample_iter(&mut rng, 1..100, 5).unwrap(); -/// println!("{:?}", sample); -/// ``` -pub fn sample_iter<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Result<Vec<T>, Vec<T>> - where I: IntoIterator<Item=T>, - R: Rng, -{ - let mut iter = iterable.into_iter(); - let mut reservoir = Vec::with_capacity(amount); - reservoir.extend(iter.by_ref().take(amount)); - - // Continue unless the iterator was exhausted - // - // note: this prevents iterators that "restart" from causing problems. - // If the iterator stops once, then so do we. - if reservoir.len() == amount { - for (i, elem) in iter.enumerate() { - let k = rng.gen_range(0, i + 1 + amount); - if let Some(spot) = reservoir.get_mut(k) { - *spot = elem; - } - } - Ok(reservoir) - } else { - // Don't hang onto extra memory. There is a corner case where - // `amount` was much less than `len(iterable)`. - reservoir.shrink_to_fit(); - Err(reservoir) - } -} - -/// Randomly sample exactly `amount` values from `slice`. -/// -/// The values are non-repeating and in random order. -/// -/// This implementation uses `O(amount)` time and memory. -/// -/// Panics if `amount > slice.len()` -/// -/// # Example -/// -/// ```rust -/// use rand::{thread_rng, seq}; -/// -/// let mut rng = thread_rng(); -/// let values = vec![5, 6, 1, 3, 4, 6, 7]; -/// println!("{:?}", seq::sample_slice(&mut rng, &values, 3)); -/// ``` -pub fn sample_slice<R, T>(rng: &mut R, slice: &[T], amount: usize) -> Vec<T> - where R: Rng, - T: Clone -{ - let indices = sample_indices(rng, slice.len(), amount); - - let mut out = Vec::with_capacity(amount); - out.extend(indices.iter().map(|i| slice[*i].clone())); - out -} - -/// Randomly sample exactly `amount` references from `slice`. -/// -/// The references are non-repeating and in random order. -/// -/// This implementation uses `O(amount)` time and memory. -/// -/// Panics if `amount > slice.len()` -/// -/// # Example -/// -/// ```rust -/// use rand::{thread_rng, seq}; -/// -/// let mut rng = thread_rng(); -/// let values = vec![5, 6, 1, 3, 4, 6, 7]; -/// println!("{:?}", seq::sample_slice_ref(&mut rng, &values, 3)); -/// ``` -pub fn sample_slice_ref<'a, R, T>(rng: &mut R, slice: &'a [T], amount: usize) -> Vec<&'a T> - where R: Rng -{ - let indices = sample_indices(rng, slice.len(), amount); - - let mut out = Vec::with_capacity(amount); - out.extend(indices.iter().map(|i| &slice[*i])); - out -} - -/// Randomly sample exactly `amount` indices from `0..length`. -/// -/// The values are non-repeating and in random order. -/// -/// This implementation uses `O(amount)` time and memory. -/// -/// This method is used internally by the slice sampling methods, but it can sometimes be useful to -/// have the indices themselves so this is provided as an alternative. -/// -/// Panics if `amount > length` -pub fn sample_indices<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize> - where R: Rng, -{ - if amount > length { - panic!("`amount` must be less than or equal to `slice.len()`"); - } - - // We are going to have to allocate at least `amount` for the output no matter what. However, - // if we use the `cached` version we will have to allocate `amount` as a HashMap as well since - // it inserts an element for every loop. - // - // Therefore, if `amount >= length / 2` then inplace will be both faster and use less memory. - // In fact, benchmarks show the inplace version is faster for length up to about 20 times - // faster than amount. - // - // TODO: there is probably even more fine-tuning that can be done here since - // `HashMap::with_capacity(amount)` probably allocates more than `amount` in practice, - // and a trade off could probably be made between memory/cpu, since hashmap operations - // are slower than array index swapping. - if amount >= length / 20 { - sample_indices_inplace(rng, length, amount) - } else { - sample_indices_cache(rng, length, amount) - } -} - -/// Sample an amount of indices using an inplace partial fisher yates method. -/// -/// This allocates the entire `length` of indices and randomizes only the first `amount`. -/// It then truncates to `amount` and returns. -/// -/// This is better than using a HashMap "cache" when `amount >= length / 2` since it does not -/// require allocating an extra cache and is much faster. -fn sample_indices_inplace<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize> - where R: Rng, -{ - debug_assert!(amount <= length); - let mut indices: Vec<usize> = Vec::with_capacity(length); - indices.extend(0..length); - for i in 0..amount { - let j: usize = rng.gen_range(i, length); - let tmp = indices[i]; - indices[i] = indices[j]; - indices[j] = tmp; - } - indices.truncate(amount); - debug_assert_eq!(indices.len(), amount); - indices -} - - -/// This method performs a partial fisher-yates on a range of indices using a HashMap -/// as a cache to record potential collisions. -/// -/// The cache avoids allocating the entire `length` of values. This is especially useful when -/// `amount <<< length`, i.e. select 3 non-repeating from 1_000_000 -fn sample_indices_cache<R>( - rng: &mut R, - length: usize, - amount: usize, -) -> Vec<usize> - where R: Rng, -{ - debug_assert!(amount <= length); - #[cfg(feature="std")] let mut cache = HashMap::with_capacity(amount); - #[cfg(not(feature="std"))] let mut cache = BTreeMap::new(); - let mut out = Vec::with_capacity(amount); - for i in 0..amount { - let j: usize = rng.gen_range(i, length); - - // equiv: let tmp = slice[i]; - let tmp = match cache.get(&i) { - Some(e) => *e, - None => i, - }; - - // equiv: slice[i] = slice[j]; - let x = match cache.get(&j) { - Some(x) => *x, - None => j, - }; - - // equiv: slice[j] = tmp; - cache.insert(j, tmp); - - // note that in the inplace version, slice[i] is automatically "returned" value - out.push(x); - } - debug_assert_eq!(out.len(), amount); - out -} - -#[cfg(test)] -mod test { - use super::*; - use {thread_rng, XorShiftRng, SeedableRng}; - - #[test] - fn test_sample_iter() { - let min_val = 1; - let max_val = 100; - - let mut r = thread_rng(); - let vals = (min_val..max_val).collect::<Vec<i32>>(); - let small_sample = sample_iter(&mut r, vals.iter(), 5).unwrap(); - let large_sample = sample_iter(&mut r, vals.iter(), vals.len() + 5).unwrap_err(); - - assert_eq!(small_sample.len(), 5); - assert_eq!(large_sample.len(), vals.len()); - // no randomization happens when amount >= len - assert_eq!(large_sample, vals.iter().collect::<Vec<_>>()); - - assert!(small_sample.iter().all(|e| { - **e >= min_val && **e <= max_val - })); - } - #[test] - fn test_sample_slice_boundaries() { - let empty: &[u8] = &[]; - - let mut r = thread_rng(); - - // sample 0 items - assert_eq!(sample_slice(&mut r, empty, 0), vec![]); - assert_eq!(sample_slice(&mut r, &[42, 2, 42], 0), vec![]); - - // sample 1 item - assert_eq!(sample_slice(&mut r, &[42], 1), vec![42]); - let v = sample_slice(&mut r, &[1, 42], 1)[0]; - assert!(v == 1 || v == 42); - - // sample "all" the items - let v = sample_slice(&mut r, &[42, 133], 2); - assert!(v == vec![42, 133] || v == vec![133, 42]); - - assert_eq!(sample_indices_inplace(&mut r, 0, 0), vec![]); - assert_eq!(sample_indices_inplace(&mut r, 1, 0), vec![]); - assert_eq!(sample_indices_inplace(&mut r, 1, 1), vec![0]); - - assert_eq!(sample_indices_cache(&mut r, 0, 0), vec![]); - assert_eq!(sample_indices_cache(&mut r, 1, 0), vec![]); - assert_eq!(sample_indices_cache(&mut r, 1, 1), vec![0]); - - // Make sure lucky 777's aren't lucky - let slice = &[42, 777]; - let mut num_42 = 0; - let total = 1000; - for _ in 0..total { - let v = sample_slice(&mut r, slice, 1); - assert_eq!(v.len(), 1); - let v = v[0]; - assert!(v == 42 || v == 777); - if v == 42 { - num_42 += 1; - } - } - let ratio_42 = num_42 as f64 / 1000 as f64; - assert!(0.4 <= ratio_42 || ratio_42 <= 0.6, "{}", ratio_42); - } - - #[test] - fn test_sample_slice() { - let xor_rng = XorShiftRng::from_seed; - - let max_range = 100; - let mut r = thread_rng(); - - for length in 1usize..max_range { - let amount = r.gen_range(0, length); - let seed: [u32; 4] = [ - r.next_u32(), r.next_u32(), r.next_u32(), r.next_u32() - ]; - - println!("Selecting indices: len={}, amount={}, seed={:?}", length, amount, seed); - - // assert that the two index methods give exactly the same result - let inplace = sample_indices_inplace( - &mut xor_rng(seed), length, amount); - let cache = sample_indices_cache( - &mut xor_rng(seed), length, amount); - assert_eq!(inplace, cache); - - // assert the basics work - let regular = sample_indices( - &mut xor_rng(seed), length, amount); - assert_eq!(regular.len(), amount); - assert!(regular.iter().all(|e| *e < length)); - assert_eq!(regular, inplace); - - // also test that sampling the slice works - let vec: Vec<usize> = (0..length).collect(); - { - let result = sample_slice(&mut xor_rng(seed), &vec, amount); - assert_eq!(result, regular); - } - - { - let result = sample_slice_ref(&mut xor_rng(seed), &vec, amount); - let expected = regular.iter().map(|v| v).collect::<Vec<_>>(); - assert_eq!(result, expected); - } - } - } -} diff --git a/vendor/rand/utils/ziggurat_tables.py b/vendor/rand/utils/ziggurat_tables.py deleted file mode 100755 index 762f956..0000000 --- a/vendor/rand/utils/ziggurat_tables.py +++ /dev/null @@ -1,127 +0,0 @@ -#!/usr/bin/env python -# -# 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. - -# This creates the tables used for distributions implemented using the -# ziggurat algorithm in `rand::distributions;`. They are -# (basically) the tables as used in the ZIGNOR variant (Doornik 2005). -# They are changed rarely, so the generated file should be checked in -# to git. -# -# It creates 3 tables: X as in the paper, F which is f(x_i), and -# F_DIFF which is f(x_i) - f(x_{i-1}). The latter two are just cached -# values which is not done in that paper (but is done in other -# variants). Note that the adZigR table is unnecessary because of -# algebra. -# -# It is designed to be compatible with Python 2 and 3. - -from math import exp, sqrt, log, floor -import random - -# The order should match the return value of `tables` -TABLE_NAMES = ['X', 'F'] - -# The actual length of the table is 1 more, to stop -# index-out-of-bounds errors. This should match the bitwise operation -# to find `i` in `zigurrat` in `libstd/rand/mod.rs`. Also the *_R and -# *_V constants below depend on this value. -TABLE_LEN = 256 - -# equivalent to `zigNorInit` in Doornik2005, but generalised to any -# distribution. r = dR, v = dV, f = probability density function, -# f_inv = inverse of f -def tables(r, v, f, f_inv): - # compute the x_i - xvec = [0]*(TABLE_LEN+1) - - xvec[0] = v / f(r) - xvec[1] = r - - for i in range(2, TABLE_LEN): - last = xvec[i-1] - xvec[i] = f_inv(v / last + f(last)) - - # cache the f's - fvec = [0]*(TABLE_LEN+1) - for i in range(TABLE_LEN+1): - fvec[i] = f(xvec[i]) - - return xvec, fvec - -# Distributions -# N(0, 1) -def norm_f(x): - return exp(-x*x/2.0) -def norm_f_inv(y): - return sqrt(-2.0*log(y)) - -NORM_R = 3.6541528853610088 -NORM_V = 0.00492867323399 - -NORM = tables(NORM_R, NORM_V, - norm_f, norm_f_inv) - -# Exp(1) -def exp_f(x): - return exp(-x) -def exp_f_inv(y): - return -log(y) - -EXP_R = 7.69711747013104972 -EXP_V = 0.0039496598225815571993 - -EXP = tables(EXP_R, EXP_V, - exp_f, exp_f_inv) - - -# Output the tables/constants/types - -def render_static(name, type, value): - # no space or - return 'pub static %s: %s =%s;\n' % (name, type, value) - -# static `name`: [`type`, .. `len(values)`] = -# [values[0], ..., values[3], -# values[4], ..., values[7], -# ... ]; -def render_table(name, values): - rows = [] - # 4 values on each row - for i in range(0, len(values), 4): - row = values[i:i+4] - rows.append(', '.join('%.18f' % f for f in row)) - - rendered = '\n [%s]' % ',\n '.join(rows) - return render_static(name, '[f64, .. %d]' % len(values), rendered) - - -with open('ziggurat_tables.rs', 'w') as f: - f.write('''// 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. - -// Tables for distributions which are sampled using the ziggurat -// algorithm. Autogenerated by `ziggurat_tables.py`. - -pub type ZigTable = &\'static [f64, .. %d]; -''' % (TABLE_LEN + 1)) - for name, tables, r in [('NORM', NORM, NORM_R), - ('EXP', EXP, EXP_R)]: - f.write(render_static('ZIG_%s_R' % name, 'f64', ' %.18f' % r)) - for (tabname, table) in zip(TABLE_NAMES, tables): - f.write(render_table('ZIG_%s_%s' % (name, tabname), table)) |