[tor-commits] [tor-rust-dependencies/master] Update rand dependency to 0.5.0-pre.2.

commit c01ff7f539a5643a607b8c156032cef48c6fcff8 Author: Isis Lovecruft isis@torproject.org Date: Tue May 15 20:14:52 2018 +0000

Update rand dependency to 0.5.0-pre.2. --- crates/rand-0.5.0-pre.2/.cargo-checksum.json | 1 + crates/rand-0.5.0-pre.2/.travis.yml | 113 ++ crates/rand-0.5.0-pre.2/CHANGELOG.md | 369 ++++++ crates/rand-0.5.0-pre.2/CONTRIBUTING.md | 93 ++ crates/rand-0.5.0-pre.2/Cargo.toml | 72 ++ crates/rand-0.5.0-pre.2/LICENSE-APACHE | 201 ++++ crates/rand-0.5.0-pre.2/LICENSE-MIT | 25 + crates/rand-0.5.0-pre.2/README.md | 140 +++ crates/rand-0.5.0-pre.2/UPDATING.md | 260 +++++ crates/rand-0.5.0-pre.2/appveyor.yml | 39 + crates/rand-0.5.0-pre.2/benches/distributions.rs | 157 +++ crates/rand-0.5.0-pre.2/benches/generators.rs | 176 +++ crates/rand-0.5.0-pre.2/benches/misc.rs | 160 +++ crates/rand-0.5.0-pre.2/examples/monte-carlo.rs | 52 + crates/rand-0.5.0-pre.2/examples/monty-hall.rs | 117 ++ .../src/distributions/bernoulli.rs | 120 ++ .../rand-0.5.0-pre.2/src/distributions/binomial.rs | 176 +++ .../src/distributions/exponential.rs | 122 ++ crates/rand-0.5.0-pre.2/src/distributions/float.rs | 206 ++++ crates/rand-0.5.0-pre.2/src/distributions/gamma.rs | 360 ++++++ .../rand-0.5.0-pre.2/src/distributions/integer.rs | 113 ++ .../src/distributions/log_gamma.rs | 51 + crates/rand-0.5.0-pre.2/src/distributions/mod.rs | 770 +++++++++++++ .../rand-0.5.0-pre.2/src/distributions/normal.rs | 192 ++++ crates/rand-0.5.0-pre.2/src/distributions/other.rs | 215 ++++ .../rand-0.5.0-pre.2/src/distributions/poisson.rs | 157 +++ .../rand-0.5.0-pre.2/src/distributions/uniform.rs | 867 ++++++++++++++ .../src/distributions/ziggurat_tables.rs | 280 +++++ crates/rand-0.5.0-pre.2/src/lib.rs | 1189 ++++++++++++++++++++ crates/rand-0.5.0-pre.2/src/prelude.rs | 28 + crates/rand-0.5.0-pre.2/src/prng/chacha.rs | 477 ++++++++ crates/rand-0.5.0-pre.2/src/prng/hc128.rs | 463 ++++++++ crates/rand-0.5.0-pre.2/src/prng/isaac.rs | 486 ++++++++ crates/rand-0.5.0-pre.2/src/prng/isaac64.rs | 478 ++++++++ crates/rand-0.5.0-pre.2/src/prng/isaac_array.rs | 137 +++ crates/rand-0.5.0-pre.2/src/prng/mod.rs | 330 ++++++ crates/rand-0.5.0-pre.2/src/prng/xorshift.rs | 226 ++++ crates/rand-0.5.0-pre.2/src/rngs/adapter/mod.rs | 17 + crates/rand-0.5.0-pre.2/src/rngs/adapter/read.rs | 137 +++ .../rand-0.5.0-pre.2/src/rngs/adapter/reseeding.rs | 260 +++++ crates/rand-0.5.0-pre.2/src/rngs/entropy.rs | 177 +++ crates/rand-0.5.0-pre.2/src/rngs/jitter.rs | 893 +++++++++++++++ crates/rand-0.5.0-pre.2/src/rngs/mock.rs | 61 + crates/rand-0.5.0-pre.2/src/rngs/mod.rs | 184 +++ crates/rand-0.5.0-pre.2/src/rngs/os.rs | 852 ++++++++++++++ crates/rand-0.5.0-pre.2/src/rngs/small.rs | 101 ++ crates/rand-0.5.0-pre.2/src/rngs/std.rs | 83 ++ crates/rand-0.5.0-pre.2/src/rngs/thread.rs | 141 +++ crates/rand-0.5.0-pre.2/src/seq.rs | 335 ++++++ crates/rand-0.5.0-pre.2/tests/bool.rs | 23 + crates/rand-0.5.0-pre.2/utils/ci/install.sh | 49 + crates/rand-0.5.0-pre.2/utils/ci/script.sh | 29 + crates/rand-0.5.0-pre.2/utils/ziggurat_tables.py | 127 +++ 53 files changed, 12887 insertions(+)

diff --git a/crates/rand-0.5.0-pre.2/.cargo-checksum.json b/crates/rand-0.5.0-pre.2/.cargo-checksum.json new file mode 100644 index 0000000..f041b5c --- /dev/null +++ b/crates/rand-0.5.0-pre.2/.cargo-checksum.json @@ -0,0 +1 @@ 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+language: rust +sudo: false + +# We aim to test all the following in any combination: +# - standard tests, benches, documentation, all available features +# - pinned stable, latest stable, beta and nightly Rust releases +# - Linux, OS X, Android, iOS, bare metal (i.e. no_std) +# - x86_64, ARMv7, a Big-Endian arch (MIPS) +matrix: + include: + - rust: 1.22.0 + install: + script: + - cargo test --tests --no-default-features + - cargo test --package rand_core --no-default-features + - cargo test --features serde1,log + - rust: stable + os: osx + install: + script: + - cargo test --tests --no-default-features + - cargo test --package rand_core --no-default-features + - cargo test --features serde1,log + - rust: beta + install: + script: + - cargo test --tests --no-default-features + - cargo test --package rand_core --no-default-features + - cargo test --features serde1,log + - rust: nightly + install: + - cargo --list | egrep "^\s*deadlinks$" -q || cargo install cargo-deadlinks + before_script: + - pip install 'travis-cargo<0.2' --user && export PATH=$HOME/.local/bin:$PATH + script: + - cargo test --tests --no-default-features --features=alloc + - cargo test --package rand_core --no-default-features --features=alloc,serde1 + - cargo test --features serde1,log,nightly,alloc + - cargo test --all --benches + # remove cached documentation, otherwise files from previous PRs can get included + - rm -rf target/doc + - cargo doc --no-deps --all --all-features + - cargo deadlinks --dir target/doc + after_success: + - travis-cargo --only nightly doc-upload + + - rust: nightly + install: + - rustup target add wasm32-unknown-unknown + # Use cargo-update since we need a real update-or-install command + # Only install if not already installed: + #- cargo --list | egrep "\binstall-update$" -q || cargo install cargo-update + #- cargo install-update -i cargo-web + # Cargo has errors with sub-commands so ignore updating for now: + - cargo --list | egrep "^\s*web$" -q || cargo install cargo-web + script: + - cargo web test --target wasm32-unknown-unknown --nodejs --features=stdweb + + - rust: nightly + install: + - rustup target add thumbv6m-none-eabi + script: + # Bare metal target; no std; only works on nightly + - cargo build --no-default-features --target thumbv6m-none-eabi --release + + # Trust cross-built/emulated targets. We must repeat all non-default values. + - rust: stable + sudo: required + dist: trusty + services: docker + env: TARGET=x86_64-unknown-freebsd DISABLE_TESTS=1 + - rust: stable + sudo: required + dist: trusty + services: docker + env: TARGET=mips-unknown-linux-gnu + - rust: stable + sudo: required + dist: trusty + services: docker + env: TARGET=armv7-linux-androideabi DISABLE_TESTS=1 + - rust: stable + os: osx + sudo: required + dist: trusty + services: docker + env: TARGET=armv7-apple-ios DISABLE_TESTS=1 + +before_install: + - set -e + - rustup self update + +# Used by all Trust targets; others must override: +install: + - sh utils/ci/install.sh + - source ~/.cargo/env || true +script: + - bash utils/ci/script.sh + +after_script: set +e + +cache: cargo +before_cache: + # Travis can't cache files that are not readable by "others" + - chmod -R a+r $HOME/.cargo + +env: + global: + secure: "BdDntVHSompN+Qxz5Rz45VI4ZqhD72r6aPl166FADlnkIwS6N6FLWdqs51O7G5CpoMXEDvyYrjmRMZe/GYLIG9cmqmn/wUrWPO+PauGiIuG/D2dmfuUNvSTRcIe7UQLXrfP3yyfZPgqsH6pSnNEVopquQKy3KjzqepgriOJtbyY=" + +notifications: + email: + on_success: never diff --git a/crates/rand-0.5.0-pre.2/CHANGELOG.md b/crates/rand-0.5.0-pre.2/CHANGELOG.md new file mode 100644 index 0000000..4f8d06f --- /dev/null +++ b/crates/rand-0.5.0-pre.2/CHANGELOG.md @@ -0,0 +1,369 @@ +# 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](https://semver.org/spec/v2.0.0.html). + +A [separate changelog is kept for rand_core](rand_core/CHANGELOG.md). + +You may also find the [Update Guide](UPDATING.md) useful. + + +## [0.5.0] - Unreleased + +### Crate features and organisation +- Minimum Rust version update: 1.22.0. (#239) +- Create a separate `rand_core` crate. (#288) +- Deprecate `rand_derive`. (#256) +- Add `log` feature. Logging is now available in `JitterRng`, `OsRng`, `EntropyRng` and `ReseedingRng`. (#246) +- Add `serde1` feature for some PRNGs. (#189) +- `stdweb` feature for `OsRng` support on WASM via stdweb. (#272, #336) + +### `Rng` trait +- Split `Rng` in `RngCore` and `Rng` extension trait. + `next_u32`, `next_u64` and `fill_bytes` are now part of `RngCore`. (#265) +- Add `Rng::sample`. (#256) +- Deprecate `Rng::gen_weighted_bool`. (#308) +- Add `Rng::gen_bool`. (#308) +- Remove `Rng::next_f32` and `Rng::next_f64`. (#273) +- Add optimized `Rng::fill` and `Rng::try_fill` methods. (#247) +- Deprecate `Rng::gen_iter`. (#286) +- Deprecate `Rng::gen_ascii_chars`. (#279) + +### `rand_core` crate +- `rand` now depends on new `rand_core` crate (#288) +- `RngCore` and `SeedableRng` are now part of `rand_core`. (#288) +- Add modules to help implementing RNGs `impl` and `le`. (#209, #228) +- Add `Error` and `ErrorKind`. (#225) +- Add `CryptoRng` marker trait. (#273) +- Add `BlockRngCore` trait. (#281) +- Add `BlockRng` and `BlockRng64` wrappers to help implementations. (#281, #325) +- Revise the `SeedableRng` trait. (#233) +- Remove default implementations for `RngCore::next_u64` and `RngCore::fill_bytes`. (#288) +- Add `RngCore::try_fill_bytes`. (#225) + +### Other traits and types +- Add `FromEntropy` trait. (#233, #375) +- Add `SmallRng` wrapper. (#296) +- Rewrite `ReseedingRng` to only work with `BlockRngCore` (substantial performance improvement). (#281) +- Deprecate `weak_rng`. Use `SmallRng` instead. (#296) +- Deprecate `random`. (#296) +- Deprecate `AsciiGenerator`. (#279) + +### Random number generators +- Switch `StdRng` and `thread_rng` to HC-128. (#277) +- `StdRng` must now be created with `from_entropy` instead of `new` +- Change `thread_rng` reseeding threshold to 32 MiB. (#277) +- PRNGs no longer implement `Copy`. (#209) +- `Debug` implementations no longer show internals. (#209) +- Implement serialization for `XorShiftRng`, `IsaacRng` and `Isaac64Rng` under the `serde1` feature. (#189) +- Implement `BlockRngCore` for `ChaChaCore` and `Hc128Core`. (#281) +- All PRNGs are now portable across big- and little-endian architectures. (#209) +- `Isaac64Rng::next_u32` no longer throws away half the results. (#209) +- Add `IsaacRng::new_from_u64` and `Isaac64Rng::new_from_u64`. (#209) +- Add the HC-128 CSPRNG `Hc128Rng`. (#210) +- Add `ChaChaRng::set_rounds` method. (#243) +- Changes to `JitterRng` to get its size down from 2112 to 24 bytes. (#251) +- Various performance improvements to all PRNGs. + +### Platform support and `OsRng` +- Add support for CloudABI. (#224) +- Remove support for NaCl. (#225) +- WASM support for `OsRng` via stdweb, behind the `stdweb` feature. (#272, #336) +- Use `getrandom` on more platforms for Linux, and on Android. (#338) +- Use the `SecRandomCopyBytes` interface on macOS. (#322) +- On systems that do not have a syscall interface, only keep a single file descriptor open for `OsRng`. (#239) +- On Unix, first try a single read from `/dev/random`, then `/dev/urandom`. (#338) +- Better error handling and reporting in `OsRng` (using new error type). (#225) +- `OsRng` now uses non-blocking when available. (#225) +- Add `EntropyRng`, which provides `OsRng`, but has `JitterRng` as a fallback. (#235) + +### Distributions +- New `Distribution` trait. (#256) +- Deprecate `Rand`, `Sample` and `IndependentSample` traits. (#256) +- Add a `Standard` distribution (replaces most `Rand` implementations). (#256) +- Add `Binomial` and `Poisson` distributions. (#96) +- Add `Alphanumeric` distribution. (#279) +- Remove `Open01` and `Closed01` distributions, use `Standard` instead (open distribution). (#274) +- Rework `Range` type, making it possible to implement it for user types. (#274) +- Add `Range::new_inclusive` for inclusive ranges. (#274) +- Add `Range::sample_single` to allow for optimized implementations. (#274) +- Use widening multiply method for much faster integer range reduction. (#274) +- `Standard` distributions for `bool` uses `Range`. (#274) +- `Standard` distributions for `bool` uses sign test. (#274) + + +## [0.4.2] - 2018-01-06 +### 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`) + + +## [0.3.20] - 2018-01-06 +### Changed +- Remove dev-dependency on `log` +- Update `fuchsia-zircon` dependency to 0.3.2 + + +## [0.3.19] - 2017-12-27 +### Changed +- Require `log <= 0.3.8` for dev builds +- Update `fuchsia-zircon` dependency to 0.3 +- Fix broken links in docs (to unblock compiler docs testing CI) + + +## [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 implementations 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! +- 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/crates/rand-0.5.0-pre.2/CONTRIBUTING.md b/crates/rand-0.5.0-pre.2/CONTRIBUTING.md new file mode 100644 index 0000000..37c1a9d --- /dev/null +++ b/crates/rand-0.5.0-pre.2/CONTRIBUTING.md @@ -0,0 +1,93 @@ +# Contributing to Rand + +Thank you for your interest in contributing to Rand! + +The following is a list of notes and tips for when you want to contribute to +Rand with a pull request. + +If you want to make major changes, it is usually best to open an issue to +discuss the idea first. + +Rand doesn't (yet) use rustfmt. It is best to follow the style of the +surrounding code, and try to keep an 80 character line limit. + + +## Documentation + +We especially welcome documentation PRs. + +As of Rust 1.25 there are differences in how stable and nightly render +documentation links. Make sure it works on stable, then nightly should be good +too. One Travis CI build checks for dead links using `cargo-deadlinks`. If you +want to run the check locally: +```sh +cargo install cargo-deadlinks +# It is recommended to remove left-over files from previous compilations +rm -rf /target/doc +cargo doc --no-deps +cargo deadlinks --dir target/doc +``` + +When making changes to code examples in the documentation, make sure they build +with: +```sh +cargo test --doc +``` + +A helpful command to rebuild documentation automatically on save (only works on +Linux): +``` +while inotifywait -r -e close_write src/ rand_core/; do cargo doc; done +``` + + +## Testing + +Rand already contains a number of unit tests, but could use more. Also the +existing ones could use clean-up. Any work on the tests is appreciated. + +Not every change or new bit of functionality requires tests, but if you can +think of a test that adds value, please add it. + +Depending on the code you change, test with one of: +```sh +cargo test +cargo test --package rand_core +# Test log, serde and 128-bit support +cargo test --features serde1,log,nightly +``` + +We want to be able to not only run the unit tests with `std` available, but also +without. Because `thread_rng()` and `FromEntropy` are not available without the +`std` feature, you may have to disable a new test with `#[cfg(feature="std")]`. +In other cases using `::test::rng` with a constant seed is a good option: +```rust +let mut rng = ::test::rng(528); // just pick some number +``` + +Only the unit tests should work in `no_std` mode, we don't want to complicate +the doc-tests. Run the tests with: +```sh +# Test no_std support +cargo test --lib --no-default-features +cargo test --package rand_core --no-default-features + +# Test no_std+alloc support; requires nightly +cargo test --lib --no-default-features --features alloc +``` + + +## Benchmarking + +A lot of code in Rand is performance-sensitive, most of it is expected to be +used in hot loops in some libraries/applications. If you change code in the +`rngs`, `prngs` or `distributions` modules, especially when you see an 'obvious +cleanup', make sure the benchmarks do not regress. It is nice to report the +benchmark results in the PR (or to report nothing's changed). + +```sh +# Benchmarks (requires nightly) +cargo bench +# Some benchmarks have a faster path with i128_support +cargo bench --features=nightly +``` diff --git a/crates/rand-0.5.0-pre.2/Cargo.toml b/crates/rand-0.5.0-pre.2/Cargo.toml new file mode 100644 index 0000000..893bf0d --- /dev/null +++ b/crates/rand-0.5.0-pre.2/Cargo.toml @@ -0,0 +1,72 @@ +# 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 = "0.5.0-pre.2" +authors = ["The Rust Project Developers"] +description = "Random number generators and other randomness functionality.\n" +homepage = "https://crates.io/crates/rand" +documentation = "https://docs.rs/rand" +readme = "README.md" +keywords = ["random", "rng"] +categories = ["algorithms", "no-std"] +license = "MIT/Apache-2.0" +repository = "https://github.com/rust-lang-nursery/rand" +[package.metadata.docs.rs] +all-features = true +[dependencies.log] +version = "0.4" +optional = true + +[dependencies.rand_core] +version = "0.2.0-pre.0" +default-features = false + +[dependencies.serde] +version = "1" +optional = true + +[dependencies.serde_derive] +version = "1" +optional = true +[dev-dependencies.bincode] +version = "1.0" + +[features] +alloc = ["rand_core/alloc"] +default = ["std"] +i128_support = [] +nightly = ["i128_support"] +serde1 = ["serde", "serde_derive", "rand_core/serde1"] +std = ["rand_core/std", "alloc", "libc", "winapi", "cloudabi", "fuchsia-zircon"] +[target."cfg(target_os = "cloudabi")".dependencies.cloudabi] +version = "0.0.3" +optional = true +[target."cfg(target_os = "fuchsia")".dependencies.fuchsia-zircon] +version = "0.3.2" +optional = true +[target."cfg(unix)".dependencies.libc] +version = "0.2" +optional = true +[target."cfg(windows)".dependencies.winapi] +version = "0.3" +features = ["minwindef", "ntsecapi", "profileapi", "winnt"] +optional = true +[target.wasm32-unknown-unknown.dependencies.stdweb] +version = "0.4" +optional = true +[badges.appveyor] +repository = "alexcrichton/rand" + +[badges.travis-ci] +repository = "rust-lang-nursery/rand" diff --git a/crates/rand-0.5.0-pre.2/LICENSE-APACHE b/crates/rand-0.5.0-pre.2/LICENSE-APACHE new file mode 100644 index 0000000..17d7468 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/LICENSE-APACHE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + https://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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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/crates/rand-0.5.0-pre.2/README.md b/crates/rand-0.5.0-pre.2/README.md new file mode 100644 index 0000000..a418598 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/README.md @@ -0,0 +1,140 @@ +# Rand + +[%5D(https://t...) +[ +[%5D(https://crates.io/crates/rand) +[%5D(https://docs.rs/rand) +[%5D(https://github.com/r...) + +A Rust library for random number generation. + +Rand provides utilities to generate random numbers, to convert them to useful +types and distributions, and some randomness-related algorithms. + +The core random number generation traits of Rand live in the [rand_core]( +https://crates.io/crates/rand_core) crate; this crate is most useful when +implementing RNGs. + +API reference: +[master branch](https://rust-lang-nursery.github.io/rand/rand/index.html), +[by release](https://docs.rs/rand/0.5). + +## Usage + +Add this to your `Cargo.toml`: + +```toml +[dependencies] +rand = "0.5.0-pre.1" +``` + +and this to your crate root: + +```rust +extern crate rand; + +use rand::prelude::*; + +// basic usage with random(): +let x: u8 = random(); +println!("{}", x); + +let y = random::<f64>(); +println!("{}", y); + +if random() { // generates a boolean + println!("Heads!"); +} + +// normal usage needs both an RNG and a function to generate the appropriate +// type, range, distribution, etc. +let mut rng = thread_rng(); +if rng.gen() { // random bool + let x: f64 = rng.gen(); // random number in range (0, 1) + println!("x is: {}", x); + let char = rng.gen::<char>(); // Sometimes you need type annotation + println!("char is: {}", char); + println!("Number from 0 to 9: {}", rng.gen_range(0, 10)); +} +``` + +## Functionality + +The Rand crate provides: + +- A convenient to use default RNG, `thread_rng`: an automatically seeded, + crypto-grade generator stored in thread-local memory. +- Pseudo-random number generators: `StdRng`, `SmallRng`, `prng` module. +- Functionality for seeding PRNGs: the `FromEntropy` trait, and as sources of + external randomness `EntropyRng`, `OsRng` and `JitterRng`. +- Most content from [`rand_core`](https://crates.io/crates/rand_core) + (re-exported): base random number generator traits and error-reporting types. +- 'Distributions' producing many different types of random values: + - A `Standard` distribution for integers, floats, and derived types including + tuples, arrays and `Option` + - Unbiased sampling from specified `Uniform` ranges. + - Sampling from exponential/normal/gamma distributions. + - Sampling from binomial/poisson distributions. + - `gen_bool` aka Bernoulli distribution. +- `seq`-uence related functionality: + - Sampling a subset of elements. + - Randomly shuffling a list. + + +## Versions + +Version 0.5 is the latest version and contains many breaking changes. +See [the Upgrade Guide](UPDATING.md) for guidance on updating from previous +versions. + +Version 0.4 was released in December 2017. It contains almost no breaking +changes since the 0.3 series. + +For more details, see the [changelog](CHANGELOG.md). + +### Rust version requirements + +The 0.5 release of Rand requires **Rustc version 1.22 or greater**. +Rand 0.4 and 0.3 (since approx. June 2017) require Rustc version 1.15 or +greater. Subsets of the Rand code may work with older Rust versions, but this +is not supported. + +Travis CI always has a build with a pinned version of Rustc matching the oldest +supported Rust release. The current policy is that this can be updated in any +Rand release if required, but the change must be noted in the changelog. + + +## Crate Features + +Rand is built with only the `std` feature anabled by default. The following +optional features are available: + +- `alloc` can be used instead of `std` to provide `Vec` and `Box`. +- `i128_support` enables support for generating `u128` and `i128` values. +- `log` enables some logging via the `log` crate. +- `nightly` enables all unstable features (`i128_support`). +- `serde1` enables serialization for some types, via Serde version 1. +- `stdweb` enables support for `OsRng` on WASM via stdweb. + +`no_std` mode is activated by setting `default-features = false`; this removes +functionality depending on `std`: + +- `thread_rng()`, and `random()` are not available, as they require thread-local + storage and an entropy source. +- `OsRng` and `EntropyRng` are 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 using the `FromEntropy` trait (user must provide + a seed). +- Exponential, normal and gamma type distributions are unavailable since `exp` + and `log` functions are not provided in `core`. +- The `seq`-uence module is unavailable, as it requires `Vec`. + + +# License + +Rand is distributed under the terms of both the MIT license and the +Apache License (Version 2.0). + +See [LICENSE-APACHE](LICENSE-APACHE) and [LICENSE-MIT](LICENSE-MIT) for details. diff --git a/crates/rand-0.5.0-pre.2/UPDATING.md b/crates/rand-0.5.0-pre.2/UPDATING.md new file mode 100644 index 0000000..09321b7 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/UPDATING.md @@ -0,0 +1,260 @@ +# Update Guide + +This guide gives a few more details than the [changelog], in particular giving +guidance on how to use new features and migrate away from old ones. + +[changelog]: CHANGELOG.md + +## Rand 0.5 + +The 0.5 release has quite significant changes over the 0.4 release; as such, +it may be worth reading through the following coverage of breaking changes. +This release also contains many optimisations, which are not detailed below. + +### Crates + +We have a new crate: `rand_core`! This crate houses some important traits, +`RngCore`, `BlockRngCore`, `SeedableRng` and `CryptoRng`, the error types, as +well as two modules with helpers for implementations: `le` and `impls`. It is +recommended that implementations of generators use the `rand_core` crate while +other users use only the `rand` crate, which re-exports most parts of `rand_core`. + +The `rand_derive` crate has been deprecated due to very low usage and +deprecation of `Rand`. + +### Features + +Several new Cargo feature flags have been added: + +- `alloc`, used without `std`, allows use of `Box` and `Vec` +- `serde1` adds serialization support to some PRNGs +- `log` adds logging in a few places (primarily to `OsRng` and `JitterRng`) + +### `Rng` and friends (core traits) + +`Rng` trait has been split into two traits, a "back end" `RngCore` (implemented +by generators) and a "front end" `Rng` implementing all the convenient extension +methods. + +Implementations of generators must `impl RngCore` instead. Usage of `rand_core` +for implementations is encouraged; the `rand_core::{le, impls}` modules may +prove useful. + +Users of `Rng` *who don't need to implement it* won't need to make so many +changes; often users can forget about `RngCore` and only import `Rng`. Instead +of `RngCore::next_u32()` / `next_u64()` users should prefer `Rng::gen()`, and +instead of `RngCore::fill_bytes(dest)`, `Rng::fill(dest)` can be used. + +#### `Rng` / `RngCore` methods + +To allow error handling from fallible sources (e.g. `OsRng`), a new +`RngCore::try_fill_bytes` method has been added; for example `EntropyRng` uses +this mechanism to fall back to `JitterRng` if `OsRng` fails, and various +handlers produce better error messages. +As before, the other methods will panic on failure, but since these are usually +used with algorithmic generators which are usually infallible, this is +considered an appropriate compromise. + +A few methods from the old `Rng` have been removed or deprecated: + +- `next_f32` and `next_f64`; these are no longer implementable by generators; + use `gen` instead +- `gen_iter`; users may instead use standard iterators with closures: + `::std::iter::repeat(()).map(|()| rng.gen())` +- `gen_ascii_chars`; use `repeat` as above and `rng.sample(Alphanumeric)` +- `gen_weighted_bool(n)`; use `gen_bool(1.0 / n)` instead + +`Rng` has a few new methods: + +- `sample(distr)` is a shortcut for `distr.sample(rng)` for any `Distribution` +- `gen_bool(p)` generates a boolean with probability `p` of being true +- `fill` and `try_fill`, corresponding to `fill_bytes` and `try_fill_bytes` + respectively (i.e. the only difference is error handling); these can fill + and integer slice / array directly, and provide better performance + than `gen()` + +#### Constructing PRNGs + +##### New randomly-initialised PRNGs + +A new trait has been added: `FromEntropy`. This is automatically implemented for +any type supporting `SeedableRng`, and provides construction from fresh, strong +entropy: + +```rust +use rand::{ChaChaRng, FromEntropy}; + +let mut rng = ChaChaRng::from_entropy(); +``` + +##### Seeding PRNGs + +The `SeedableRng` trait has been modified to include the seed type via an +associated type (`SeedableRng::Seed`) instead of a template parameter +(`SeedableRng<Seed>`). Additionally, all PRNGs now seed from a byte-array +(`[u8; N]` for some fixed N). This allows generic handling of PRNG seeding +which was not previously possible. + +PRNGs are no longer constructed from other PRNGs via `Rand` support / `gen()`, +but through `SeedableRng::from_rng`, which allows error handling and is +intentionally explicit. + +`SeedableRng::reseed` has been removed since it has no utility over `from_seed` +and its performance advantage is questionable. + +Implementations of `SeedableRng` may need to change their `Seed` type to a +byte-array; this restriction has been made to ensure portable handling of +Endianness. Helper functions are available in `rand_core::le` to read `u32` and +`u64` values from byte arrays. + +#### Block-based PRNGs + +rand_core has a new helper trait, `BlockRngCore`, and implementation, +`BlockRng`. These are for use by generators which generate a block of random +data at a time instead of word-sized values. Using this trait and implementation +has two advantages: optimised `RngCore` methods are provided, and the PRNG can +be used with `ReseedingRng` with very low overhead. + +#### Cryptographic RNGs + +A new trait has been added: `CryptoRng`. This is purely a marker trait to +indicate which generators should be suitable for cryptography, e.g. +`fn foo<R: Rng + CryptoRng>(rng: &mut R)`. *Suitability for cryptographic +use cannot be guaranteed.* + +### Error handling + +A new `Error` type has been added, designed explicitly for no-std compatibility, +simplicity, and enough flexibility for our uses (carrying a `cause` when +possible): +```rust +pub struct Error { + pub kind: ErrorKind, + pub msg: &'static str, + // some fields omitted +} +``` +The associated `ErrorKind` allows broad classification of errors into permanent, +unexpected, transient and not-yet-ready kinds. + +The following use the new error type: + +- `RngCore::try_fill_bytes` +- `Rng::try_fill` +- `OsRng::new` +- `JitterRng::new` + +### External generators + +We have a new generator, `EntropyRng`, which wraps `OsRng` and `JitterRng` +(preferring to use the former, but falling back to the latter if necessary). +This allows easy construction with fallback via `SeedableRng::from_rng`, +e.g. `IsaacRng::from_rng(EntropyRng::new())?`. This is equivalent to using +`FromEntropy` except for error handling. + +It is recommended to use `EntropyRng` over `OsRng` to avoid errors on platforms +with broken system generator, but it should be noted that the `JitterRng` +fallback is very slow. + +### PRNGs + +*Pseudo-Random Number Generators* (i.e. deterministic algorithmic generators) +have had a few changes since 0.4, and are now housed in the `prng` module +(old names remain temporarily available for compatibility; eventually these +generators will likely be housed outside the `rand` crate). + +All PRNGs now do not implement `Copy` to prevent accidental copying of the +generator's state (and thus repetitions of generated values). Explicit cloning +via `Clone` is still available. All PRNGs now have a custom implementation of +`Debug` which does not print any internal state; this helps avoid accidentally +leaking cryptographic generator state in log files. External PRNG +implementations are advised to follow this pattern (see also doc on `RngCore`). + +`SmallRng` has been added as a wrapper, currently around `XorShiftRng` (but +likely another algorithm soon). This is for uses where small state and fast +initialisation are important but cryptographic strength is not required. +(Actual performance of generation varies by benchmark; dependending on usage +this may or may not be the fastest algorithm, but will always be fast.) + +#### `ReseedingRng` + +The `ReseedingRng` wrapper has been signficantly altered to reduce overhead. +Unfortunately the new `ReseedingRng` is not compatible with all RNGs, but only +those using `BlockRngCore`. + +#### ISAAC PRNGs + +The `IsaacRng` and `Isaac64Rng` PRNGs now have an additional construction +method: `new_from_u64(seed)`. 64 bits of state is insufficient for cryptography +but may be of use in simulations and games. This will likely be superceeded by +a method to construct any PRNG from any hashable object in the future. + +#### HC-128 + +This is a new cryptographic generator, selected as one of the "stream ciphers +suitable for widespread adoption" by eSTREAM. This is now the default +cryptographic generator, used by `StdRng` and `thread_rng()`. + +### Helper functions/traits + +The `Rand` trait has been deprecated. Instead, users are encouraged to use +`Standard` which is a real distribution and supports the same sampling as +`Rand`. `Rng::gen()` now uses `Standard` and should work exactly as before. +See the documentation of the `distributions` module on how to implement +`Distribution<T>` for `Standard` for user types `T` + +`weak_rng()` has been deprecated; use `SmallRng::from_entropy()` instead. + +### Distributions + +The `Sample` and `IndependentSample` traits have been replaced by a single +trait, `Distribution`. This is largely equivalent to `IndependentSample`, but +with `ind_sample` replaced by just `sample`. Support for mutable distributions +has been dropped; although it appears there may be a few genuine uses, these +are not used widely enough to justify the existance of two independent traits +or of having to provide mutable access to a distribution object. Both `Sample` +and `IndependentSample` are still available, but deprecated; they will be +removed in a future release. + +`Distribution::sample` (as well as several other functions) can now be called +directly on type-erased (unsized) RNGs. + +`RandSample` has been removed (see `Rand` deprecation and new `Standard` +distribution). + +The `Closed01` wrapper has been removed, but `OpenClosed01` has been added. + +#### Uniform distributions + +Two new distributions are available: + +- `Standard` produces uniformly-distributed samples for many different types, + and acts as a replacement for `Rand` +- `Alphanumeric` samples `char`s from the ranges `a-z A-Z 0-9` + +##### Ranges + +The `Range` distribution has been heavily adapted, and renamed to `Uniform`: + +- `Uniform::new(low, high)` remains (half open `[low, high)`) +- `Uniform::new_inclusive(low, high)` has been added, including `high` in the sample range +- `Uniform::sample_single(low, high, rng)` is a faster variant for single usage sampling from `[low, high)` + +`Uniform` can now be implemented for user-defined types; see the `uniform` module. + +#### Non-uniform distributions + +Two distributions have been added: + +- Poisson, modelling the number of events expected from a constant-rate + source within a fixed time interval (e.g. nuclear decay) +- Binomial, modelling the outcome of a fixed number of yes-no trials + +The sampling methods are based on those in "Numerical Recipes in C". + +##### Exponential and Normal distributions + +The main `Exp` and `Normal` distributions are unchanged, however the +"standard" versions, `Exp1` and `StandardNormal` are no longer wrapper types, +but full distributions. Instead of writing `let Exp1(x) = rng.gen();` you now +write `let x = rng.sample(Exp1);`. diff --git a/crates/rand-0.5.0-pre.2/appveyor.yml b/crates/rand-0.5.0-pre.2/appveyor.yml new file mode 100644 index 0000000..97d3ce6 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/appveyor.yml @@ -0,0 +1,39 @@ +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 --all # cannot use --all and --features together + - cargo test --all --benches + - cargo test --features serde1,log,nightly + - cargo test --tests --no-default-features --features=alloc,serde1 + - cargo test --package rand_core --no-default-features --features=alloc,serde1 diff --git a/crates/rand-0.5.0-pre.2/benches/distributions.rs b/crates/rand-0.5.0-pre.2/benches/distributions.rs new file mode 100644 index 0000000..d456df3 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/benches/distributions.rs @@ -0,0 +1,157 @@ +#![feature(test)] +#![cfg_attr(all(feature="i128_support", feature="nightly"), allow(stable_features))] // stable since 2018-03-27 +#![cfg_attr(all(feature="i128_support", feature="nightly"), feature(i128_type, i128))] + +extern crate test; +extern crate rand; + +const RAND_BENCH_N: u64 = 1000; + +use std::mem::size_of; +use test::{black_box, Bencher}; + +use rand::{Rng, FromEntropy, XorShiftRng}; +use rand::distributions::*; + +macro_rules! distr_int { + ($fnn:ident, $ty:ty, $distr:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + let distr = $distr; + + b.iter(|| { + let mut accum = 0 as $ty; + for _ in 0..::RAND_BENCH_N { + let x: $ty = distr.sample(&mut rng); + accum = accum.wrapping_add(x); + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N; + } + } +} + +macro_rules! distr_float { + ($fnn:ident, $ty:ty, $distr:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + let distr = $distr; + + b.iter(|| { + let mut accum = 0.0; + for _ in 0..::RAND_BENCH_N { + let x: $ty = distr.sample(&mut rng); + accum += x; + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N; + } + } +} + +macro_rules! distr { + ($fnn:ident, $ty:ty, $distr:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + let distr = $distr; + + b.iter(|| { + for _ in 0..::RAND_BENCH_N { + let x: $ty = distr.sample(&mut rng); + black_box(x); + } + }); + b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N; + } + } +} + +// uniform +distr_int!(distr_uniform_i8, i8, Uniform::new(20i8, 100)); +distr_int!(distr_uniform_i16, i16, Uniform::new(-500i16, 2000)); +distr_int!(distr_uniform_i32, i32, Uniform::new(-200_000_000i32, 800_000_000)); +distr_int!(distr_uniform_i64, i64, Uniform::new(3i64, 123_456_789_123)); +#[cfg(feature = "i128_support")] +distr_int!(distr_uniform_i128, i128, Uniform::new(-123_456_789_123i128, 123_456_789_123_456_789)); + +distr_float!(distr_uniform_f32, f32, Uniform::new(2.26f32, 2.319)); +distr_float!(distr_uniform_f64, f64, Uniform::new(2.26f64, 2.319)); + +// standard +distr_int!(distr_standard_i8, i8, Standard); +distr_int!(distr_standard_i16, i16, Standard); +distr_int!(distr_standard_i32, i32, Standard); +distr_int!(distr_standard_i64, i64, Standard); +#[cfg(feature = "i128_support")] +distr_int!(distr_standard_i128, i128, Standard); + +distr!(distr_standard_bool, bool, Standard); +distr!(distr_standard_alphanumeric, char, Alphanumeric); +distr!(distr_standard_codepoint, char, Standard); + +distr_float!(distr_standard_f32, f32, Standard); +distr_float!(distr_standard_f64, f64, Standard); +distr_float!(distr_open01_f32, f32, Open01); +distr_float!(distr_open01_f64, f64, Open01); +distr_float!(distr_openclosed01_f32, f32, OpenClosed01); +distr_float!(distr_openclosed01_f64, f64, OpenClosed01); + +// distributions +distr_float!(distr_exp, f64, Exp::new(1.23 * 4.56)); +distr_float!(distr_normal, f64, Normal::new(-1.23, 4.56)); +distr_float!(distr_log_normal, f64, LogNormal::new(-1.23, 4.56)); +distr_float!(distr_gamma_large_shape, f64, Gamma::new(10., 1.0)); +distr_float!(distr_gamma_small_shape, f64, Gamma::new(0.1, 1.0)); +distr_int!(distr_binomial, u64, Binomial::new(20, 0.7)); +distr_int!(distr_poisson, u64, Poisson::new(4.0)); + + +// construct and sample from a range +macro_rules! gen_range_int { + ($fnn:ident, $ty:ident, $low:expr, $high:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + + b.iter(|| { + let mut high = $high; + let mut accum: $ty = 0; + for _ in 0..::RAND_BENCH_N { + accum = accum.wrapping_add(rng.gen_range($low, high)); + // force recalculation of range each time + high = high.wrapping_add(1) & std::$ty::MAX; + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N; + } + } +} + +gen_range_int!(gen_range_i8, i8, -20i8, 100); +gen_range_int!(gen_range_i16, i16, -500i16, 2000); +gen_range_int!(gen_range_i32, i32, -200_000_000i32, 800_000_000); +gen_range_int!(gen_range_i64, i64, 3i64, 123_456_789_123); +#[cfg(feature = "i128_support")] +gen_range_int!(gen_range_i128, i128, -12345678901234i128, 123_456_789_123_456_789); + +#[bench] +fn dist_iter(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + let distr = Normal::new(-2.71828, 3.14159); + let mut iter = distr.sample_iter(&mut rng); + + b.iter(|| { + let mut accum = 0.0; + for _ in 0..::RAND_BENCH_N { + accum += iter.next().unwrap(); + } + black_box(accum); + }); + b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N; +} diff --git a/crates/rand-0.5.0-pre.2/benches/generators.rs b/crates/rand-0.5.0-pre.2/benches/generators.rs new file mode 100644 index 0000000..b2f4dbc --- /dev/null +++ b/crates/rand-0.5.0-pre.2/benches/generators.rs @@ -0,0 +1,176 @@ +#![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::prelude::*; +use rand::prng::{XorShiftRng, Hc128Rng, IsaacRng, Isaac64Rng, ChaChaRng}; +use rand::prng::hc128::Hc128Core; +use rand::rngs::adapter::ReseedingRng; +use rand::rngs::{OsRng, JitterRng, EntropyRng}; + +macro_rules! gen_bytes { + ($fnn:ident, $gen:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = $gen; + let mut buf = [0u8; BYTES_LEN]; + b.iter(|| { + for _ in 0..RAND_BENCH_N { + rng.fill_bytes(&mut buf); + black_box(buf); + } + }); + b.bytes = BYTES_LEN as u64 * RAND_BENCH_N; + } + } +} + +gen_bytes!(gen_bytes_xorshift, XorShiftRng::from_entropy()); +gen_bytes!(gen_bytes_chacha20, ChaChaRng::from_entropy()); +gen_bytes!(gen_bytes_hc128, Hc128Rng::from_entropy()); +gen_bytes!(gen_bytes_isaac, IsaacRng::from_entropy()); +gen_bytes!(gen_bytes_isaac64, Isaac64Rng::from_entropy()); +gen_bytes!(gen_bytes_std, StdRng::from_entropy()); +gen_bytes!(gen_bytes_small, SmallRng::from_entropy()); +gen_bytes!(gen_bytes_os, OsRng::new().unwrap()); + +macro_rules! gen_uint { + ($fnn:ident, $ty:ty, $gen:expr) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = $gen; + b.iter(|| { + let mut accum: $ty = 0; + for _ in 0..RAND_BENCH_N { + accum = accum.wrapping_add(rng.gen::<$ty>()); + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N; + } + } +} + +gen_uint!(gen_u32_xorshift, u32, XorShiftRng::from_entropy()); +gen_uint!(gen_u32_chacha20, u32, ChaChaRng::from_entropy()); +gen_uint!(gen_u32_hc128, u32, Hc128Rng::from_entropy()); +gen_uint!(gen_u32_isaac, u32, IsaacRng::from_entropy()); +gen_uint!(gen_u32_isaac64, u32, Isaac64Rng::from_entropy()); +gen_uint!(gen_u32_std, u32, StdRng::from_entropy()); +gen_uint!(gen_u32_small, u32, SmallRng::from_entropy()); +gen_uint!(gen_u32_os, u32, OsRng::new().unwrap()); + +gen_uint!(gen_u64_xorshift, u64, XorShiftRng::from_entropy()); +gen_uint!(gen_u64_chacha20, u64, ChaChaRng::from_entropy()); +gen_uint!(gen_u64_hc128, u64, Hc128Rng::from_entropy()); +gen_uint!(gen_u64_isaac, u64, IsaacRng::from_entropy()); +gen_uint!(gen_u64_isaac64, u64, Isaac64Rng::from_entropy()); +gen_uint!(gen_u64_std, u64, StdRng::from_entropy()); +gen_uint!(gen_u64_small, u64, SmallRng::from_entropy()); +gen_uint!(gen_u64_os, u64, OsRng::new().unwrap()); + +// Do not test JitterRng like the others by running it RAND_BENCH_N times per, +// measurement, because it is way too slow. Only run it once. +#[bench] +fn gen_u64_jitter(b: &mut Bencher) { + let mut rng = JitterRng::new().unwrap(); + b.iter(|| { + black_box(rng.gen::<u64>()); + }); + b.bytes = size_of::<u64>() as u64; +} + +macro_rules! init_gen { + ($fnn:ident, $gen:ident) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = XorShiftRng::from_entropy(); + b.iter(|| { + let r2 = $gen::from_rng(&mut rng).unwrap(); + black_box(r2); + }); + } + } +} + +init_gen!(init_xorshift, XorShiftRng); +init_gen!(init_hc128, Hc128Rng); +init_gen!(init_isaac, IsaacRng); +init_gen!(init_isaac64, Isaac64Rng); +init_gen!(init_chacha, ChaChaRng); + +#[bench] +fn init_jitter(b: &mut Bencher) { + b.iter(|| { + black_box(JitterRng::new().unwrap()); + }); +} + + +const RESEEDING_THRESHOLD: u64 = 1024*1024*1024; // something high enough to get + // deterministic measurements + +#[bench] +fn reseeding_hc128_bytes(b: &mut Bencher) { + let mut rng = ReseedingRng::new(Hc128Core::from_entropy(), + RESEEDING_THRESHOLD, + EntropyRng::new()); + let mut buf = [0u8; BYTES_LEN]; + b.iter(|| { + for _ in 0..RAND_BENCH_N { + rng.fill_bytes(&mut buf); + black_box(buf); + } + }); + b.bytes = BYTES_LEN as u64 * RAND_BENCH_N; +} + +macro_rules! reseeding_uint { + ($fnn:ident, $ty:ty) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = ReseedingRng::new(Hc128Core::from_entropy(), + RESEEDING_THRESHOLD, + EntropyRng::new()); + b.iter(|| { + let mut accum: $ty = 0; + for _ in 0..RAND_BENCH_N { + accum = accum.wrapping_add(rng.gen::<$ty>()); + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N; + } + } +} + +reseeding_uint!(reseeding_hc128_u32, u32); +reseeding_uint!(reseeding_hc128_u64, u64); + + +macro_rules! threadrng_uint { + ($fnn:ident, $ty:ty) => { + #[bench] + fn $fnn(b: &mut Bencher) { + let mut rng = thread_rng(); + b.iter(|| { + let mut accum: $ty = 0; + for _ in 0..RAND_BENCH_N { + accum = accum.wrapping_add(rng.gen::<$ty>()); + } + black_box(accum); + }); + b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N; + } + } +} + +threadrng_uint!(thread_rng_u32, u32); +threadrng_uint!(thread_rng_u64, u64); diff --git a/crates/rand-0.5.0-pre.2/benches/misc.rs b/crates/rand-0.5.0-pre.2/benches/misc.rs new file mode 100644 index 0000000..1d17cc5 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/benches/misc.rs @@ -0,0 +1,160 @@ +#![feature(test)] + +extern crate test; +extern crate rand; + +const RAND_BENCH_N: u64 = 1000; + +use test::{black_box, Bencher}; + +use rand::prelude::*; +use rand::seq::*; + +#[bench] +fn misc_gen_bool_const(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + // Can be evaluated at compile time. + let mut accum = true; + for _ in 0..::RAND_BENCH_N { + accum ^= rng.gen_bool(0.18); + } + accum + }) +} + +#[bench] +fn misc_gen_bool_var(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + let mut p = 0.18; + black_box(&mut p); // Avoid constant folding. + for _ in 0..::RAND_BENCH_N { + black_box(rng.gen_bool(p)); + } + }) +} + +#[bench] +fn misc_bernoulli_const(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + let d = rand::distributions::Bernoulli::new(0.18); + b.iter(|| { + // Can be evaluated at compile time. + let mut accum = true; + for _ in 0..::RAND_BENCH_N { + accum ^= rng.sample(d); + } + accum + }) +} + +#[bench] +fn misc_bernoulli_var(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + let mut p = 0.18; + black_box(&mut p); // Avoid constant folding. + let d = rand::distributions::Bernoulli::new(p); + for _ in 0..::RAND_BENCH_N { + black_box(rng.sample(d)); + } + }) +} + +#[bench] +fn misc_shuffle_100(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(thread_rng()).unwrap(); + 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 = SmallRng::from_rng(thread_rng()).unwrap(); + 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 = SmallRng::from_rng(thread_rng()).unwrap(); + 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 = SmallRng::from_rng(thread_rng()).unwrap(); + 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 = SmallRng::from_rng(thread_rng()).unwrap(); + 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); + +#[bench] +fn gen_1k_iter_repeat(b: &mut Bencher) { + use std::iter; + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + let v: Vec<u64> = iter::repeat(()).map(|()| rng.gen()).take(128).collect(); + black_box(v); + }); + b.bytes = 1024; +} + +#[bench] +#[allow(deprecated)] +fn gen_1k_gen_iter(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + let v: Vec<u64> = rng.gen_iter().take(128).collect(); + black_box(v); + }); + b.bytes = 1024; +} + +#[bench] +fn gen_1k_sample_iter(b: &mut Bencher) { + use rand::distributions::{Distribution, Standard}; + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + b.iter(|| { + let v: Vec<u64> = Standard.sample_iter(&mut rng).take(128).collect(); + black_box(v); + }); + b.bytes = 1024; +} + +#[bench] +fn gen_1k_fill(b: &mut Bencher) { + let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap(); + let mut buf = [0u64; 128]; + b.iter(|| { + rng.fill(&mut buf[..]); + black_box(buf); + }); + b.bytes = 1024; +} diff --git a/crates/rand-0.5.0-pre.2/examples/monte-carlo.rs b/crates/rand-0.5.0-pre.2/examples/monte-carlo.rs new file mode 100644 index 0000000..c18108a --- /dev/null +++ b/crates/rand-0.5.0-pre.2/examples/monte-carlo.rs @@ -0,0 +1,52 @@ +// Copyright 2013-2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! # Monte Carlo estimation of π +//! +//! Imagine that we have a square with sides of length 2 and a unit circle +//! (radius = 1), both centered at the origin. The areas are: +//! +//! ```text +//! area of circle = πr² = π * r * r = π +//! area of square = 2² = 4 +//! ``` +//! +//! The circle is entirely within the square, 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. + +#![cfg(feature="std")] + + +extern crate rand; + +use rand::distributions::{Distribution, Uniform}; + +fn main() { + let range = Uniform::new(-1.0f64, 1.0); + let mut rng = rand::thread_rng(); + + let total = 1_000_000; + let mut in_circle = 0; + + for _ in 0..total { + let a = range.sample(&mut rng); + let b = range.sample(&mut rng); + if a*a + b*b <= 1.0 { + in_circle += 1; + } + } + + // prints something close to 3.14159... + println!("π is approximately {}", 4. * (in_circle as f64) / (total as f64)); +} diff --git a/crates/rand-0.5.0-pre.2/examples/monty-hall.rs b/crates/rand-0.5.0-pre.2/examples/monty-hall.rs new file mode 100644 index 0000000..3750f8f --- /dev/null +++ b/crates/rand-0.5.0-pre.2/examples/monty-hall.rs @@ -0,0 +1,117 @@ +// Copyright 2013-2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! ## 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]: https://en.wikipedia.org/wiki/Monty_Hall_problem + +#![cfg(feature="std")] + + +extern crate rand; + +use rand::Rng; +use rand::distributions::{Distribution, Uniform}; + +struct SimulationResult { + win: bool, + switch: bool, +} + +// Run a single simulation of the Monty Hall problem. +fn simulate<R: Rng>(random_door: &Uniform<u32>, rng: &mut R) + -> SimulationResult { + let car = random_door.sample(rng); + + // This is our initial choice + let mut choice = random_door.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 } +} + +// 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 = Uniform::new(0u32, 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); +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/bernoulli.rs b/crates/rand-0.5.0-pre.2/src/distributions/bernoulli.rs new file mode 100644 index 0000000..2361fac --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/bernoulli.rs @@ -0,0 +1,120 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. +//! The Bernoulli distribution. + +use Rng; +use distributions::Distribution; + +/// The Bernoulli distribution. +/// +/// This is a special case of the Binomial distribution where `n = 1`. +/// +/// # Example +/// +/// ```rust +/// use rand::distributions::{Bernoulli, Distribution}; +/// +/// let d = Bernoulli::new(0.3); +/// let v = d.sample(&mut rand::thread_rng()); +/// println!("{} is from a Bernoulli distribution", v); +/// ``` +/// +/// # Precision +/// +/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`), +/// so only probabilities that are multiples of 2^-64 can be +/// represented. +#[derive(Clone, Copy, Debug)] +pub struct Bernoulli { + /// Probability of success, relative to the maximal integer. + p_int: u64, +} + +impl Bernoulli { + /// Construct a new `Bernoulli` with the given probability of success `p`. + /// + /// # Panics + /// + /// If `p < 0` or `p > 1`. + /// + /// # Precision + /// + /// For `p = 1.0`, the resulting distribution will always generate true. + /// For `p = 0.0`, the resulting distribution will always generate false. + /// + /// This method is accurate for any input `p` in the range `[0, 1]` which is + /// a multiple of 2^-64. (Note that not all multiples of + /// 2^-64 in `[0, 1]` can be represented as a `f64`.) + #[inline] + pub fn new(p: f64) -> Bernoulli { + assert!((p >= 0.0) & (p <= 1.0), "Bernoulli::new not called with 0 <= p <= 0"); + // Technically, this should be 2^64 or `u64::MAX + 1` because we compare + // using `<` when sampling. However, `u64::MAX` rounds to an `f64` + // larger than `u64::MAX` anyway. + const MAX_P_INT: f64 = ::core::u64::MAX as f64; + let p_int = if p < 1.0 { + (p * MAX_P_INT) as u64 + } else { + // Avoid overflow: `MAX_P_INT` cannot be represented as u64. + ::core::u64::MAX + }; + Bernoulli { p_int } + } +} + +impl Distribution<bool> for Bernoulli { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool { + // Make sure to always return true for p = 1.0. + if self.p_int == ::core::u64::MAX { + return true; + } + let r: u64 = rng.gen(); + r < self.p_int + } +} + +#[cfg(test)] +mod test { + use Rng; + use distributions::Distribution; + use super::Bernoulli; + + #[test] + fn test_trivial() { + let mut r = ::test::rng(1); + let always_false = Bernoulli::new(0.0); + let always_true = Bernoulli::new(1.0); + for _ in 0..5 { + assert_eq!(r.sample::<bool, _>(&always_false), false); + assert_eq!(r.sample::<bool, _>(&always_true), true); + assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false); + assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true); + } + } + + #[test] + fn test_average() { + const P: f64 = 0.3; + let d = Bernoulli::new(P); + const N: u32 = 10_000_000; + + let mut sum: u32 = 0; + let mut rng = ::test::rng(2); + for _ in 0..N { + if d.sample(&mut rng) { + sum += 1; + } + } + let avg = (sum as f64) / (N as f64); + + assert!((avg - P).abs() < 1e-3); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/binomial.rs b/crates/rand-0.5.0-pre.2/src/distributions/binomial.rs new file mode 100644 index 0000000..7e4e869 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/binomial.rs @@ -0,0 +1,176 @@ +// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The binomial distribution. + +use Rng; +use distributions::Distribution; +use distributions::log_gamma::log_gamma; +use std::f64::consts::PI; + +/// The binomial distribution `Binomial(n, p)`. +/// +/// This distribution has density function: +/// `f(k) = n!/(k! (n-k)!) p^k (1-p)^(n-k)` for `k >= 0`. +/// +/// # Example +/// +/// ``` +/// use rand::distributions::{Binomial, Distribution}; +/// +/// let bin = Binomial::new(20, 0.3); +/// let v = bin.sample(&mut rand::thread_rng()); +/// println!("{} is from a binomial distribution", v); +/// ``` +#[derive(Clone, Copy, Debug)] +pub struct Binomial { + /// Number of trials. + n: u64, + /// Probability of success. + p: f64, +} + +impl Binomial { + /// Construct a new `Binomial` with the given shape parameters `n` (number + /// of trials) and `p` (probability of success). + /// + /// Panics if `p <= 0` or `p >= 1`. + pub fn new(n: u64, p: f64) -> Binomial { + assert!(p > 0.0, "Binomial::new called with p <= 0"); + assert!(p < 1.0, "Binomial::new called with p >= 1"); + Binomial { n, p } + } +} + +impl Distribution<u64> for Binomial { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 { + // binomial distribution is symmetrical with respect to p -> 1-p, k -> n-k + // switch p so that it is less than 0.5 - this allows for lower expected values + // we will just invert the result at the end + let p = if self.p <= 0.5 { + self.p + } else { + 1.0 - self.p + }; + + // expected value of the sample + let expected = self.n as f64 * p; + + let result = + // for low expected values we just simulate n drawings + if expected < 25.0 { + let mut lresult = 0.0; + for _ in 0 .. self.n { + if rng.gen_bool(p) { + lresult += 1.0; + } + } + lresult + } + // high expected value - do the rejection method + else { + // prepare some cached values + let float_n = self.n as f64; + let ln_fact_n = log_gamma(float_n + 1.0); + let pc = 1.0 - p; + let log_p = p.ln(); + let log_pc = pc.ln(); + let sq = (expected * (2.0 * pc)).sqrt(); + + let mut lresult; + + loop { + let mut comp_dev: f64; + // we use the lorentzian distribution as the comparison distribution + // f(x) ~ 1/(1+x/^2) + loop { + // draw from the lorentzian distribution + comp_dev = (PI*rng.gen::<f64>()).tan(); + // shift the peak of the comparison ditribution + lresult = expected + sq * comp_dev; + // repeat the drawing until we are in the range of possible values + if lresult >= 0.0 && lresult < float_n + 1.0 { + break; + } + } + + // the result should be discrete + lresult = lresult.floor(); + + let log_binomial_dist = ln_fact_n - log_gamma(lresult+1.0) - + log_gamma(float_n - lresult + 1.0) + lresult*log_p + (float_n - lresult)*log_pc; + // this is the binomial probability divided by the comparison probability + // we will generate a uniform random value and if it is larger than this, + // we interpret it as a value falling out of the distribution and repeat + let comparison_coeff = (log_binomial_dist.exp() * sq) * (1.2 * (1.0 + comp_dev*comp_dev)); + + if comparison_coeff >= rng.gen() { + break; + } + } + + lresult + }; + + // invert the result for p < 0.5 + if p != self.p { + self.n - result as u64 + } else { + result as u64 + } + } +} + +#[cfg(test)] +mod test { + use Rng; + use distributions::Distribution; + use super::Binomial; + + fn test_binomial_mean_and_variance<R: Rng>(n: u64, p: f64, rng: &mut R) { + let binomial = Binomial::new(n, p); + + let expected_mean = n as f64 * p; + let expected_variance = n as f64 * p * (1.0 - p); + + let mut results = [0.0; 1000]; + for i in results.iter_mut() { *i = binomial.sample(rng) as f64; } + + let mean = results.iter().sum::<f64>() / results.len() as f64; + assert!((mean as f64 - expected_mean).abs() < expected_mean / 50.0); + + let variance = + results.iter().map(|x| (x - mean) * (x - mean)).sum::<f64>() + / results.len() as f64; + assert!((variance - expected_variance).abs() < expected_variance / 10.0); + } + + #[test] + fn test_binomial() { + let mut rng = ::test::rng(123); + test_binomial_mean_and_variance(150, 0.1, &mut rng); + test_binomial_mean_and_variance(70, 0.6, &mut rng); + test_binomial_mean_and_variance(40, 0.5, &mut rng); + test_binomial_mean_and_variance(20, 0.7, &mut rng); + test_binomial_mean_and_variance(20, 0.5, &mut rng); + } + + #[test] + #[should_panic] + fn test_binomial_invalid_lambda_zero() { + Binomial::new(20, 0.0); + } + + #[test] + #[should_panic] + fn test_binomial_invalid_lambda_neg() { + Binomial::new(20, -10.0); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/exponential.rs b/crates/rand-0.5.0-pre.2/src/distributions/exponential.rs new file mode 100644 index 0000000..de6564e --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/exponential.rs @@ -0,0 +1,122 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The exponential distribution. + +use {Rng}; +use distributions::{ziggurat, ziggurat_tables, Distribution}; + +/// Samples floating-point numbers according to the exponential distribution, +/// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or +/// sampling with `-rng.gen::<f64>().ln()`, but faster. +/// +/// 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*](https://www.doornik.com/research/ziggurat.pdf). Nuffield +/// College, Oxford +/// +/// # Example +/// ``` +/// use rand::prelude::*; +/// use rand::distributions::Exp1; +/// +/// let val: f64 = SmallRng::from_entropy().sample(Exp1); +/// println!("{}", val); +/// ``` +#[derive(Clone, Copy, Debug)] +pub struct Exp1; + +// This could be done via `-rng.gen::<f64>().ln()` but that is slower. +impl Distribution<f64> for Exp1 { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + #[inline] + fn pdf(x: f64) -> f64 { + (-x).exp() + } + #[inline] + fn zero_case<R: Rng + ?Sized>(rng: &mut R, _u: f64) -> f64 { + ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln() + } + + 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 +/// +/// ``` +/// use rand::distributions::{Exp, Distribution}; +/// +/// let exp = Exp::new(2.0); +/// let v = exp.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 Distribution<f64> for Exp { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + let n: f64 = rng.sample(Exp1); + n * self.lambda_inverse + } +} + +#[cfg(test)] +mod test { + use distributions::Distribution; + use super::Exp; + + #[test] + fn test_exp() { + let exp = Exp::new(10.0); + let mut rng = ::test::rng(221); + for _ in 0..1000 { + assert!(exp.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/crates/rand-0.5.0-pre.2/src/distributions/float.rs b/crates/rand-0.5.0-pre.2/src/distributions/float.rs new file mode 100644 index 0000000..0058122 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/float.rs @@ -0,0 +1,206 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Basic floating-point number distributions + +use core::mem; +use Rng; +use distributions::{Distribution, Standard}; + +/// A distribution to sample floating point numbers uniformly in the half-open +/// interval `(0, 1]`, i.e. including 1 but not 0. +/// +/// All values that can be generated are of the form `n * ε/2`. For `f32` +/// the 23 most significant random bits of a `u32` are used and for `f64` the +/// 53 most significant bits of a `u64` are used. The conversion uses the +/// multiplicative method. +/// +/// See also: [`Standard`] which samples from `[0, 1)`, [`Open01`] +/// which samples from `(0, 1)` and [`Uniform`] which samples from arbitrary +/// ranges. +/// +/// # Example +/// ``` +/// use rand::{thread_rng, Rng}; +/// use rand::distributions::OpenClosed01; +/// +/// let val: f32 = thread_rng().sample(OpenClosed01); +/// println!("f32 from (0, 1): {}", val); +/// ``` +/// +/// [`Standard`]: struct.Standard.html +/// [`Open01`]: struct.Open01.html +/// [`Uniform`]: uniform/struct.Uniform.html +#[derive(Clone, Copy, Debug)] +pub struct OpenClosed01; + +/// A distribution to sample floating point numbers uniformly in the open +/// interval `(0, 1)`, i.e. not including either endpoint. +/// +/// All values that can be generated are of the form `n * ε + ε/2`. For `f32` +/// the 22 most significant random bits of an `u32` are used, for `f64` 52 from +/// an `u64`. The conversion uses a transmute-based method. +/// +/// See also: [`Standard`] which samples from `[0, 1)`, [`OpenClosed01`] +/// which samples from `(0, 1]` and [`Uniform`] which samples from arbitrary +/// ranges. +/// +/// # Example +/// ``` +/// use rand::{thread_rng, Rng}; +/// use rand::distributions::Open01; +/// +/// let val: f32 = thread_rng().sample(Open01); +/// println!("f32 from (0, 1): {}", val); +/// ``` +/// +/// [`Standard`]: struct.Standard.html +/// [`OpenClosed01`]: struct.OpenClosed01.html +/// [`Uniform`]: uniform/struct.Uniform.html +#[derive(Clone, Copy, Debug)] +pub struct Open01; + + +pub(crate) trait IntoFloat { + type F; + + /// Helper method to combine the fraction and a contant exponent into a + /// float. + /// + /// Only the least significant bits of `self` may be set, 23 for `f32` and + /// 52 for `f64`. + /// The resulting value will fall in a range that depends on the exponent. + /// As an example the range with exponent 0 will be + /// [2⁰..2¹), which is [1..2). + fn into_float_with_exponent(self, exponent: i32) -> Self::F; +} + +macro_rules! float_impls { + ($ty:ty, $uty:ty, $fraction_bits:expr, $exponent_bias:expr) => { + impl IntoFloat for $uty { + type F = $ty; + #[inline(always)] + fn into_float_with_exponent(self, exponent: i32) -> $ty { + // The exponent is encoded using an offset-binary representation + let exponent_bits = + (($exponent_bias + exponent) as $uty) << $fraction_bits; + unsafe { mem::transmute(self | exponent_bits) } + } + } + + impl Distribution<$ty> for Standard { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty { + // Multiply-based method; 24/53 random bits; [0, 1) interval. + // We use the most significant bits because for simple RNGs + // those are usually more random. + let float_size = mem::size_of::<$ty>() * 8; + let precision = $fraction_bits + 1; + let scale = 1.0 / ((1 as $uty << precision) as $ty); + + let value: $uty = rng.gen(); + scale * (value >> (float_size - precision)) as $ty + } + } + + impl Distribution<$ty> for OpenClosed01 { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty { + // Multiply-based method; 24/53 random bits; (0, 1] interval. + // We use the most significant bits because for simple RNGs + // those are usually more random. + let float_size = mem::size_of::<$ty>() * 8; + let precision = $fraction_bits + 1; + let scale = 1.0 / ((1 as $uty << precision) as $ty); + + let value: $uty = rng.gen(); + let value = value >> (float_size - precision); + // Add 1 to shift up; will not overflow because of right-shift: + scale * (value + 1) as $ty + } + } + + impl Distribution<$ty> for Open01 { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty { + // Transmute-based method; 23/52 random bits; (0, 1) interval. + // We use the most significant bits because for simple RNGs + // those are usually more random. + const EPSILON: $ty = 1.0 / (1u64 << $fraction_bits) as $ty; + let float_size = mem::size_of::<$ty>() * 8; + + let value: $uty = rng.gen(); + let fraction = value >> (float_size - $fraction_bits); + fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0) + } + } + } +} +float_impls! { f32, u32, 23, 127 } +float_impls! { f64, u64, 52, 1023 } + + +#[cfg(test)] +mod tests { + use Rng; + use distributions::{Open01, OpenClosed01}; + use rngs::mock::StepRng; + + const EPSILON32: f32 = ::core::f32::EPSILON; + const EPSILON64: f64 = ::core::f64::EPSILON; + + #[test] + fn standard_fp_edge_cases() { + let mut zeros = StepRng::new(0, 0); + assert_eq!(zeros.gen::<f32>(), 0.0); + assert_eq!(zeros.gen::<f64>(), 0.0); + + let mut one32 = StepRng::new(1 << 8, 0); + assert_eq!(one32.gen::<f32>(), EPSILON32 / 2.0); + + let mut one64 = StepRng::new(1 << 11, 0); + assert_eq!(one64.gen::<f64>(), EPSILON64 / 2.0); + + let mut max = StepRng::new(!0, 0); + assert_eq!(max.gen::<f32>(), 1.0 - EPSILON32 / 2.0); + assert_eq!(max.gen::<f64>(), 1.0 - EPSILON64 / 2.0); + } + + #[test] + fn openclosed01_edge_cases() { + let mut zeros = StepRng::new(0, 0); + assert_eq!(zeros.sample::<f32, _>(OpenClosed01), 0.0 + EPSILON32 / 2.0); + assert_eq!(zeros.sample::<f64, _>(OpenClosed01), 0.0 + EPSILON64 / 2.0); + + let mut one32 = StepRng::new(1 << 8, 0); + assert_eq!(one32.sample::<f32, _>(OpenClosed01), EPSILON32); + + let mut one64 = StepRng::new(1 << 11, 0); + assert_eq!(one64.sample::<f64, _>(OpenClosed01), EPSILON64); + + let mut max = StepRng::new(!0, 0); + assert_eq!(max.sample::<f32, _>(OpenClosed01), 1.0); + assert_eq!(max.sample::<f64, _>(OpenClosed01), 1.0); + } + + #[test] + fn open01_edge_cases() { + let mut zeros = StepRng::new(0, 0); + assert_eq!(zeros.sample::<f32, _>(Open01), 0.0 + EPSILON32 / 2.0); + assert_eq!(zeros.sample::<f64, _>(Open01), 0.0 + EPSILON64 / 2.0); + + let mut one32 = StepRng::new(1 << 9, 0); + assert_eq!(one32.sample::<f32, _>(Open01), EPSILON32 / 2.0 * 3.0); + + let mut one64 = StepRng::new(1 << 12, 0); + assert_eq!(one64.sample::<f64, _>(Open01), EPSILON64 / 2.0 * 3.0); + + let mut max = StepRng::new(!0, 0); + assert_eq!(max.sample::<f32, _>(Open01), 1.0 - EPSILON32 / 2.0); + assert_eq!(max.sample::<f64, _>(Open01), 1.0 - EPSILON64 / 2.0); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/gamma.rs b/crates/rand-0.5.0-pre.2/src/distributions/gamma.rs new file mode 100644 index 0000000..44e1c59 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/gamma.rs @@ -0,0 +1,360 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The Gamma and derived distributions. + +use self::GammaRepr::*; +use self::ChiSquaredRepr::*; + +use Rng; +use distributions::normal::StandardNormal; +use distributions::{Distribution, Exp, Open01}; + +/// 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 +/// +/// ``` +/// use rand::distributions::{Distribution, Gamma}; +/// +/// let gamma = Gamma::new(2.0, 5.0); +/// let v = gamma.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](https://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 } + } +} + +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, + c: 1. / (9. * d).sqrt(), + d + } + } +} + +impl Distribution<f64> for Gamma { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + match self.repr { + Small(ref g) => g.sample(rng), + One(ref g) => g.sample(rng), + Large(ref g) => g.sample(rng), + } + } +} +impl Distribution<f64> for GammaSmallShape { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + let u: f64 = rng.sample(Open01); + + self.large_shape.sample(rng) * u.powf(self.inv_shape) + } +} +impl Distribution<f64> for GammaLargeShape { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + loop { + let x = rng.sample(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 u: f64 = rng.sample(Open01); + + 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 +/// +/// ``` +/// use rand::distributions::{ChiSquared, Distribution}; +/// +/// let chi = ChiSquared::new(11.0); +/// let v = chi.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 } + } +} +impl Distribution<f64> for ChiSquared { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + match self.repr { + DoFExactlyOne => { + // k == 1 => N(0,1)^2 + let norm = rng.sample(StandardNormal); + norm * norm + } + DoFAnythingElse(ref g) => g.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 +/// +/// ``` +/// use rand::distributions::{FisherF, Distribution}; +/// +/// let f = FisherF::new(2.0, 32.0); +/// let v = f.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 Distribution<f64> for FisherF { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + self.numer.sample(rng) / self.denom.sample(rng) * self.dof_ratio + } +} + +/// The Student t distribution, `t(nu)`, where `nu` is the degrees of +/// freedom. +/// +/// # Example +/// +/// ``` +/// use rand::distributions::{StudentT, Distribution}; +/// +/// let t = StudentT::new(11.0); +/// let v = t.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 Distribution<f64> for StudentT { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + let norm = rng.sample(StandardNormal); + norm * (self.dof / self.chi.sample(rng)).sqrt() + } +} + +#[cfg(test)] +mod test { + use distributions::Distribution; + use super::{ChiSquared, StudentT, FisherF}; + + #[test] + fn test_chi_squared_one() { + let chi = ChiSquared::new(1.0); + let mut rng = ::test::rng(201); + for _ in 0..1000 { + chi.sample(&mut rng); + } + } + #[test] + fn test_chi_squared_small() { + let chi = ChiSquared::new(0.5); + let mut rng = ::test::rng(202); + for _ in 0..1000 { + chi.sample(&mut rng); + } + } + #[test] + fn test_chi_squared_large() { + let chi = ChiSquared::new(30.0); + let mut rng = ::test::rng(203); + for _ in 0..1000 { + chi.sample(&mut rng); + } + } + #[test] + #[should_panic] + fn test_chi_squared_invalid_dof() { + ChiSquared::new(-1.0); + } + + #[test] + fn test_f() { + let f = FisherF::new(2.0, 32.0); + let mut rng = ::test::rng(204); + for _ in 0..1000 { + f.sample(&mut rng); + } + } + + #[test] + fn test_t() { + let t = StudentT::new(11.0); + let mut rng = ::test::rng(205); + for _ in 0..1000 { + t.sample(&mut rng); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/integer.rs b/crates/rand-0.5.0-pre.2/src/distributions/integer.rs new file mode 100644 index 0000000..a23ddd5 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/integer.rs @@ -0,0 +1,113 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The implementations of the `Standard` distribution for integer types. + +use {Rng}; +use distributions::{Distribution, Standard}; + +impl Distribution<u8> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 { + rng.next_u32() as u8 + } +} + +impl Distribution<u16> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u16 { + rng.next_u32() as u16 + } +} + +impl Distribution<u32> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u32 { + rng.next_u32() + } +} + +impl Distribution<u64> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 { + rng.next_u64() + } +} + +#[cfg(feature = "i128_support")] +impl Distribution<u128> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u128 { + // Use LE; we explicitly generate one value before the next. + let x = rng.next_u64() as u128; + let y = rng.next_u64() as u128; + (y << 64) | x + } +} + +impl Distribution<usize> for Standard { + #[inline] + #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize { + rng.next_u32() as usize + } + + #[inline] + #[cfg(target_pointer_width = "64")] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize { + rng.next_u64() as usize + } +} + +macro_rules! impl_int_from_uint { + ($ty:ty, $uty:ty) => { + impl Distribution<$ty> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty { + rng.gen::<$uty>() as $ty + } + } + } +} + +impl_int_from_uint! { i8, u8 } +impl_int_from_uint! { i16, u16 } +impl_int_from_uint! { i32, u32 } +impl_int_from_uint! { i64, u64 } +#[cfg(feature = "i128_support")] impl_int_from_uint! { i128, u128 } +impl_int_from_uint! { isize, usize } + + +#[cfg(test)] +mod tests { + use Rng; + use distributions::{Standard}; + + #[test] + fn test_integers() { + let mut rng = ::test::rng(806); + + rng.sample::<isize, _>(Standard); + rng.sample::<i8, _>(Standard); + rng.sample::<i16, _>(Standard); + rng.sample::<i32, _>(Standard); + rng.sample::<i64, _>(Standard); + #[cfg(feature = "i128_support")] + rng.sample::<i128, _>(Standard); + + rng.sample::<usize, _>(Standard); + rng.sample::<u8, _>(Standard); + rng.sample::<u16, _>(Standard); + rng.sample::<u32, _>(Standard); + rng.sample::<u64, _>(Standard); + #[cfg(feature = "i128_support")] + rng.sample::<u128, _>(Standard); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/log_gamma.rs b/crates/rand-0.5.0-pre.2/src/distributions/log_gamma.rs new file mode 100644 index 0000000..f1fa383 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/log_gamma.rs @@ -0,0 +1,51 @@ +// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +/// Calculates ln(gamma(x)) (natural logarithm of the gamma +/// function) using the Lanczos approximation. +/// +/// The approximation expresses the gamma function as: +/// `gamma(z+1) = sqrt(2*pi)*(z+g+0.5)^(z+0.5)*exp(-z-g-0.5)*Ag(z)` +/// `g` is an arbitrary constant; we use the approximation with `g=5`. +/// +/// Noting that `gamma(z+1) = z*gamma(z)` and applying `ln` to both sides: +/// `ln(gamma(z)) = (z+0.5)*ln(z+g+0.5)-(z+g+0.5) + ln(sqrt(2*pi)*Ag(z)/z)` +/// +/// `Ag(z)` is an infinite series with coefficients that can be calculated +/// ahead of time - we use just the first 6 terms, which is good enough +/// for most purposes. +pub fn log_gamma(x: f64) -> f64 { + // precalculated 6 coefficients for the first 6 terms of the series + let coefficients: [f64; 6] = [ + 76.18009172947146, + -86.50532032941677, + 24.01409824083091, + -1.231739572450155, + 0.1208650973866179e-2, + -0.5395239384953e-5, + ]; + + // (x+0.5)*ln(x+g+0.5)-(x+g+0.5) + let tmp = x + 5.5; + let log = (x + 0.5) * tmp.ln() - tmp; + + // the first few terms of the series for Ag(x) + let mut a = 1.000000000190015; + let mut denom = x; + for coeff in &coefficients { + denom += 1.0; + a += coeff / denom; + } + + // get everything together + // a is Ag(x) + // 2.5066... is sqrt(2pi) + log + (2.5066282746310005 * a / x).ln() +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/mod.rs b/crates/rand-0.5.0-pre.2/src/distributions/mod.rs new file mode 100644 index 0000000..6519516 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/mod.rs @@ -0,0 +1,770 @@ +// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Generating random samples from probability distributions. +//! +//! This module is the home of the [`Distribution`] trait and several of its +//! implementations. It is the workhorse behind some of the convenient +//! functionality of the [`Rng`] trait, including [`gen`], [`gen_range`] and +//! of course [`sample`]. +//! +//! Abstractly, a [probability distribution] describes the probability of +//! occurance of each value in its sample space. +//! +//! More concretely, an implementation of `Distribution<T>` for type `X` is an +//! algorithm for choosing values from the sample space (a subset of `T`) +//! according to the distribution `X` represents, using an external source of +//! randomness (an RNG supplied to the `sample` function). +//! +//! A type `X` may implement `Distribution<T>` for multiple types `T`. +//! Any type implementing [`Distribution`] is stateless (i.e. immutable), +//! but it may have internal parameters set at construction time (for example, +//! [`Uniform`] allows specification of its sample space as a range within `T`). +//! +//! +//! # The `Standard` distribution +//! +//! The [`Standard`] distribution is important to mention. This is the +//! distribution used by [`Rng::gen()`] and represents the "default" way to +//! produce a random value for many different types, including most primitive +//! types, tuples, arrays, and a few derived types. See the documentation of +//! [`Standard`] for more details. +//! +//! Implementing `Distribution<T>` for [`Standard`] for user types `T` makes it +//! possible to generate type `T` with [`Rng::gen()`], and by extension also +//! with the [`random()`] function. +//! +//! +//! # Distribution to sample from a `Uniform` range +//! +//! The [`Uniform`] distribution is more flexible than [`Standard`], but also +//! more specialised: it supports fewer target types, but allows the sample +//! space to be specified as an arbitrary range within its target type `T`. +//! Both [`Standard`] and [`Uniform`] are in some sense uniform distributions. +//! +//! Values may be sampled from this distribution using [`Rng::gen_range`] or +//! by creating a distribution object with [`Uniform::new`], +//! [`Uniform::new_inclusive`] or `From<Range>`. When the range limits are not +//! known at compile time it is typically faster to reuse an existing +//! distribution object than to call [`Rng::gen_range`]. +//! +//! User types `T` may also implement `Distribution<T>` for [`Uniform`], +//! although this is less straightforward than for [`Standard`] (see the +//! documentation in the [`uniform` module]. Doing so enables generation of +//! values of type `T` with [`Rng::gen_range`]. +//! +//! +//! # Other distributions +//! +//! There are surprisingly many ways to uniformly generate random floats. A +//! range between 0 and 1 is standard, but the exact bounds (open vs closed) +//! and accuracy differ. In addition to the [`Standard`] distribution Rand offers +//! [`Open01`] and [`OpenClosed01`]. See [Floating point implementation] for +//! more details. +//! +//! [`Alphanumeric`] is a simple distribution to sample random letters and +//! numbers of the `char` type; in contrast [`Standard`] may sample any valid +//! `char`. +//! +//! +//! # Non-uniform probability distributions +//! +//! Rand currently provides the following probability distributions: +//! +//! - Related to real-valued quantities that grow linearly +//! (e.g. errors, offsets): +//! - [`Normal`] distribution, and [`StandardNormal`] as a primitive +//! - Related to Bernoulli trials (yes/no events, with a given probability): +//! - [`Binomial`] distribution +//! - [`Bernoulli`] distribution, similar to [`Rng::gen_bool`]. +//! - Related to positive real-valued quantities that grow exponentially +//! (e.g. prices, incomes, populations): +//! - [`LogNormal`] distribution +//! - Related to rate of occurrance of indenpendant events: +//! with a given rate) +//! - [`Poisson`] distribution +//! - [`Exp`]onential distribution, and [`Exp1`] as a primitive +//! - Gamma and derived distributions: +//! - [`Gamma`] distribution +//! - [`ChiSquared`] distribution +//! - [`StudentT`] distribution +//! - [`FisherF`] distribution +//! +//! +//! # Examples +//! +//! Sampling from a distribution: +//! +//! ``` +//! use rand::{thread_rng, Rng}; +//! use rand::distributions::Exp; +//! +//! let exp = Exp::new(2.0); +//! let v = thread_rng().sample(exp); +//! println!("{} is from an Exp(2) distribution", v); +//! ``` +//! +//! Implementing the [`Standard`] distribution for a user type: +//! +//! ``` +//! # #![allow(dead_code)] +//! use rand::Rng; +//! use rand::distributions::{Distribution, Standard}; +//! +//! struct MyF32 { +//! x: f32, +//! } +//! +//! impl Distribution<MyF32> for Standard { +//! fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 { +//! MyF32 { x: rng.gen() } +//! } +//! } +//! ``` +//! +//! +//! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution +//! [`Distribution`]: trait.Distribution.html +//! [`gen_range`]: ../trait.Rng.html#method.gen_range +//! [`gen`]: ../trait.Rng.html#method.gen +//! [`sample`]: ../trait.Rng.html#method.sample +//! [`new_inclusive`]: struct.Uniform.html#method.new_inclusive +//! [`random()`]: ../fn.random.html +//! [`Rng::gen_bool`]: ../trait.Rng.html#method.gen_bool +//! [`Rng::gen_range`]: ../trait.Rng.html#method.gen_range +//! [`Rng::gen()`]: ../trait.Rng.html#method.gen +//! [`Rng`]: ../trait.Rng.html +//! [`sample_iter`]: trait.Distribution.html#method.sample_iter +//! [`uniform` module]: uniform/index.html +//! [Floating point implementation]: struct.Standard.html#floating-point-implementation +// distributions +//! [`Alphanumeric`]: struct.Alphanumeric.html +//! [`Bernoulli`]: struct.Bernoulli.html +//! [`Binomial`]: struct.Binomial.html +//! [`ChiSquared`]: struct.ChiSquared.html +//! [`Exp`]: struct.Exp.html +//! [`Exp1`]: struct.Exp1.html +//! [`FisherF`]: struct.FisherF.html +//! [`Gamma`]: struct.Gamma.html +//! [`LogNormal`]: struct.LogNormal.html +//! [`Normal`]: struct.Normal.html +//! [`Open01`]: struct.Open01.html +//! [`OpenClosed01`]: struct.OpenClosed01.html +//! [`Poisson`]: struct.Poisson.html +//! [`Standard`]: struct.Standard.html +//! [`StandardNormal`]: struct.StandardNormal.html +//! [`StudentT`]: struct.StudentT.html +//! [`Uniform`]: struct.Uniform.html + +use Rng; + +#[doc(inline)] pub use self::other::Alphanumeric; +#[doc(inline)] pub use self::uniform::Uniform; +#[doc(inline)] pub use self::float::{OpenClosed01, Open01}; +#[deprecated(since="0.5.0", note="use Uniform instead")] +pub use self::uniform::Uniform as Range; +#[cfg(feature="std")] +#[doc(inline)] pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT}; +#[cfg(feature="std")] +#[doc(inline)] pub use self::normal::{Normal, LogNormal, StandardNormal}; +#[cfg(feature="std")] +#[doc(inline)] pub use self::exponential::{Exp, Exp1}; +#[cfg(feature = "std")] +#[doc(inline)] pub use self::poisson::Poisson; +#[cfg(feature = "std")] +#[doc(inline)] pub use self::binomial::Binomial; +#[doc(inline)] pub use self::bernoulli::Bernoulli; + +pub mod uniform; +#[cfg(feature="std")] +#[doc(hidden)] pub mod gamma; +#[cfg(feature="std")] +#[doc(hidden)] pub mod normal; +#[cfg(feature="std")] +#[doc(hidden)] pub mod exponential; +#[cfg(feature = "std")] +#[doc(hidden)] pub mod poisson; +#[cfg(feature = "std")] +#[doc(hidden)] pub mod binomial; +#[doc(hidden)] pub mod bernoulli; + +mod float; +mod integer; +#[cfg(feature="std")] +mod log_gamma; +mod other; +#[cfg(feature="std")] +mod ziggurat_tables; +#[cfg(feature="std")] +use distributions::float::IntoFloat; + +/// Types that can be used to create a random instance of `Support`. +#[deprecated(since="0.5.0", note="use Distribution instead")] +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. +#[allow(deprecated)] +#[deprecated(since="0.5.0", note="use Distribution instead")] +pub trait IndependentSample<Support>: Sample<Support> { + /// Generate a random value. + fn ind_sample<R: Rng>(&self, &mut R) -> Support; +} + +/// DEPRECATED: Use `distributions::uniform` instead. +#[deprecated(since="0.5.0", note="use uniform instead")] +pub mod range { + pub use distributions::uniform::Uniform as Range; + pub use distributions::uniform::SampleUniform as SampleRange; +} + +#[allow(deprecated)] +mod impls { + use Rng; + use distributions::{Distribution, Sample, IndependentSample, + WeightedChoice}; + #[cfg(feature="std")] + use distributions::exponential::Exp; + #[cfg(feature="std")] + use distributions::gamma::{Gamma, ChiSquared, FisherF, StudentT}; + #[cfg(feature="std")] + use distributions::normal::{Normal, LogNormal}; + use distributions::range::{Range, SampleRange}; + + impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> { + fn sample<R: Rng>(&mut self, rng: &mut R) -> T { + Distribution::sample(self, rng) + } + } + impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> { + fn ind_sample<R: Rng>(&self, rng: &mut R) -> T { + Distribution::sample(self, rng) + } + } + + impl<T: SampleRange> Sample<T> for Range<T> { + fn sample<R: Rng>(&mut self, rng: &mut R) -> T { + Distribution::sample(self, rng) + } + } + impl<T: SampleRange> IndependentSample<T> for Range<T> { + fn ind_sample<R: Rng>(&self, rng: &mut R) -> T { + Distribution::sample(self, rng) + } + } + + #[cfg(feature="std")] + macro_rules! impl_f64 { + ($($name: ident), *) => { + $( + impl Sample<f64> for $name { + fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { + Distribution::sample(self, rng) + } + } + impl IndependentSample<f64> for $name { + fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { + Distribution::sample(self, rng) + } + } + )* + } + } + #[cfg(feature="std")] + impl_f64!(Exp, Gamma, ChiSquared, FisherF, StudentT, Normal, LogNormal); +} + +/// Types (distributions) that can be used to create a random instance of `T`. +/// +/// It is possible to sample from a distribution through both the +/// [`Distribution`] and [`Rng`] traits, via `distr.sample(&mut rng)` and +/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which +/// produces an iterator that samples from the distribution. +/// +/// All implementations are expected to be immutable; this has the significant +/// advantage of not needing to consider thread safety, and for most +/// distributions efficient state-less sampling algorithms are available. +pub trait Distribution<T> { + /// Generate a random value of `T`, using `rng` as the source of randomness. + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T; + + /// Create an iterator that generates random values of `T`, using `rng` as + /// the source of randomness. + /// + /// # Example + /// + /// ``` + /// use rand::thread_rng; + /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard}; + /// + /// let mut rng = thread_rng(); + /// + /// // Vec of 16 x f32: + /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect(); + /// + /// // String: + /// let s: String = Alphanumeric.sample_iter(&mut rng).take(7).collect(); + /// + /// // Dice-rolling: + /// let die_range = Uniform::new_inclusive(1, 6); + /// let mut roll_die = die_range.sample_iter(&mut rng); + /// while roll_die.next().unwrap() != 6 { + /// println!("Not a 6; rolling again!"); + /// } + /// ``` + fn sample_iter<'a, R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> + where Self: Sized, R: Rng + { + DistIter { + distr: self, + rng: rng, + phantom: ::core::marker::PhantomData, + } + } +} + +impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T { + (*self).sample(rng) + } +} + + +/// An iterator that generates random values of `T` with distribution `D`, +/// using `R` as the source of randomness. +/// +/// This `struct` is created by the [`sample_iter`] method on [`Distribution`]. +/// See its documentation for more. +/// +/// [`Distribution`]: trait.Distribution.html +/// [`sample_iter`]: trait.Distribution.html#method.sample_iter +#[derive(Debug)] +pub struct DistIter<'a, D: 'a, R: 'a, T> { + distr: &'a D, + rng: &'a mut R, + phantom: ::core::marker::PhantomData<T>, +} + +impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> + where D: Distribution<T>, R: Rng + 'a +{ + type Item = T; + + #[inline(always)] + fn next(&mut self) -> Option<T> { + Some(self.distr.sample(self.rng)) + } + + fn size_hint(&self) -> (usize, Option<usize>) { + (usize::max_value(), None) + } +} + + +/// A generic random value distribution, implemented for many primitive types. +/// Usually generates values with a numerically uniform distribution, and with a +/// range appropriate to the type. +/// +/// ## Built-in Implementations +/// +/// 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)`. See notes below. +/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their +/// normal integer variants. +/// +/// The following aggregate types also implement the distribution `Standard` as +/// long as their component types implement it: +/// +/// * Tuples and arrays: Each element of the tuple or array is generated +/// independently, using the `Standard` distribution recursively. +/// * `Option<T>` where `Standard` is implemented for `T`: Returns `None` with +/// probability 0.5; otherwise generates a random `x: T` and returns `Some(x)`. +/// +/// # Example +/// ``` +/// use rand::prelude::*; +/// use rand::distributions::Standard; +/// +/// let val: f32 = SmallRng::from_entropy().sample(Standard); +/// println!("f32 from [0, 1): {}", val); +/// ``` +/// +/// # Floating point implementation +/// The floating point implementations for `Standard` generate a random value in +/// the half-open interval `[0, 1)`, i.e. including 0 but not 1. +/// +/// All values that can be generated are of the form `n * ε/2`. For `f32` +/// the 23 most significant random bits of a `u32` are used and for `f64` the +/// 53 most significant bits of a `u64` are used. The conversion uses the +/// multiplicative method: `(rng.gen::<$uty>() >> N) as $ty * (ε/2)`. +/// +/// See also: [`Open01`] which samples from `(0, 1)`, [`OpenClosed01`] which +/// samples from `(0, 1]` and `Rng::gen_range(0, 1)` which also samples from +/// `[0, 1)`. Note that `Open01` and `gen_range` (which uses [`Uniform`]) use +/// transmute-based methods which yield 1 bit less precision but may perform +/// faster on some architectures (on modern Intel CPUs all methods have +/// approximately equal performance). +/// +/// [`Open01`]: struct.Open01.html +/// [`OpenClosed01`]: struct.OpenClosed01.html +/// [`Uniform`]: uniform/struct.Uniform.html +#[derive(Clone, Copy, Debug)] +pub struct Standard; + +#[allow(deprecated)] +impl<T> ::Rand for T where Standard: Distribution<T> { + fn rand<R: Rng>(rng: &mut R) -> Self { + Standard.sample(rng) + } +} + + +/// 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 `Distribution` trait. +/// Note that `&T` is (cheaply) `Clone` for all `T`, as is `u32`, so one can +/// store references or indices into another vector. +/// +/// # Example +/// +/// ``` +/// use rand::distributions::{Weighted, WeightedChoice, Distribution}; +/// +/// 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.sample(&mut rng)); +/// } +/// ``` +#[derive(Debug)] +pub struct WeightedChoice<'a, T:'a> { + items: &'a mut [Weighted<T>], + weight_range: Uniform<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, + // we're likely to be generating numbers in this range + // relatively often, so might as well cache it + weight_range: Uniform::new(0, running_total) + } + } +} + +impl<'a, T: Clone> Distribution<T> for WeightedChoice<'a, T> { + fn sample<R: Rng + ?Sized>(&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.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; + } + 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 + ?Sized, 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 { + loop { + // As an optimisation we re-implement the conversion to a f64. + // From the remaining 12 most significant bits we use 8 to construct `i`. + // This saves us generating a whole extra random number, while the added + // precision of using 64 bits for f64 does not buy us much. + let bits = rng.next_u64(); + let i = bits as usize & 0xff; + + let u = if symmetric { + // Convert to a value in the range [2,4) and substract to get [-1,1) + // We can't convert to an open range directly, that would require + // substracting `3.0 - EPSILON`, which is not representable. + // It is possible with an extra step, but an open range does not + // seem neccesary for the ziggurat algorithm anyway. + (bits >> 12).into_float_with_exponent(1) - 3.0 + } else { + // Convert to a value in the range [1,2) and substract to get (0,1) + (bits >> 12).into_float_with_exponent(0) + - (1.0 - ::core::f64::EPSILON / 2.0) + }; + 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; + use rngs::mock::StepRng; + use super::{WeightedChoice, Weighted, Distribution}; + + #[test] + fn test_weighted_choice() { + // this makes assumptions about the internal implementation of + // WeightedChoice. It may fail when the implementation in + // `distributions::uniform::UniformInt` changes. + + macro_rules! t { + ($items:expr, $expected:expr) => {{ + let mut items = $items; + let mut total_weight = 0; + for item in &items { total_weight += item.weight; } + + let wc = WeightedChoice::new(&mut items); + let expected = $expected; + + // Use extremely large steps between the random numbers, because + // we test with small ranges and `UniformInt` is designed to prefer + // the most significant bits. + let mut rng = StepRng::new(0, !0 / (total_weight as u64)); + + for &val in expected.iter() { + assert_eq!(wc.sample(&mut rng), val) + } + }} + } + + t!([Weighted { weight: 1, item: 10}], [10]); + + // skip some + t!([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!([Weighted { weight: 4, item: 30}, + Weighted { weight: 3, item: 31}], + [30, 31, 30, 31, 30, 31, 30]); + + // check that we're binary searching + // correctly with some vectors of odd + // length. + t!([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!([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, 54, 51, 55, 52, 56, 53]); + } + + #[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 = ::core::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 }]); + } + + #[test] #[allow(deprecated)] + fn test_backwards_compat_sample() { + use distributions::{Sample, IndependentSample}; + + struct Constant<T> { val: T } + impl<T: Copy> Sample<T> for Constant<T> { + fn sample<R: Rng>(&mut self, _: &mut R) -> T { self.val } + } + impl<T: Copy> IndependentSample<T> for Constant<T> { + fn ind_sample<R: Rng>(&self, _: &mut R) -> T { self.val } + } + + let mut sampler = Constant{ val: 293 }; + assert_eq!(sampler.sample(&mut ::test::rng(233)), 293); + assert_eq!(sampler.ind_sample(&mut ::test::rng(234)), 293); + } + + #[cfg(feature="std")] + #[test] #[allow(deprecated)] + fn test_backwards_compat_exp() { + use distributions::{IndependentSample, Exp}; + let sampler = Exp::new(1.0); + sampler.ind_sample(&mut ::test::rng(235)); + } + + #[cfg(feature="std")] + #[test] + fn test_distributions_iter() { + use distributions::Normal; + let mut rng = ::test::rng(210); + let distr = Normal::new(10.0, 10.0); + let results: Vec<_> = distr.sample_iter(&mut rng).take(100).collect(); + println!("{:?}", results); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/normal.rs b/crates/rand-0.5.0-pre.2/src/distributions/normal.rs new file mode 100644 index 0000000..69ee6a0 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/normal.rs @@ -0,0 +1,192 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The normal and derived distributions. + +use Rng; +use distributions::{ziggurat, ziggurat_tables, Distribution, Open01}; + +/// Samples floating-point numbers according to the normal distribution +/// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to +/// `Normal::new(0.0, 1.0)` but faster. +/// +/// 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*](https://www.doornik.com/research/ziggurat.pdf). Nuffield +/// College, Oxford +/// +/// # Example +/// ``` +/// use rand::prelude::*; +/// use rand::distributions::StandardNormal; +/// +/// let val: f64 = SmallRng::from_entropy().sample(StandardNormal); +/// println!("{}", val); +/// ``` +#[derive(Clone, Copy, Debug)] +pub struct StandardNormal; + +impl Distribution<f64> for StandardNormal { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + #[inline] + fn pdf(x: f64) -> f64 { + (-x*x/2.0).exp() + } + #[inline] + fn zero_case<R: Rng + ?Sized>(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 x_: f64 = rng.sample(Open01); + let y_: f64 = rng.sample(Open01); + + 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 } + } + + 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 +/// +/// ``` +/// use rand::distributions::{Normal, Distribution}; +/// +/// // mean 2, standard deviation 3 +/// let normal = Normal::new(2.0, 3.0); +/// let v = normal.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, + std_dev + } + } +} +impl Distribution<f64> for Normal { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + let n = rng.sample(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 +/// +/// ``` +/// use rand::distributions::{LogNormal, Distribution}; +/// +/// // mean 2, standard deviation 3 +/// let log_normal = LogNormal::new(2.0, 3.0); +/// let v = log_normal.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 Distribution<f64> for LogNormal { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 { + self.norm.sample(rng).exp() + } +} + +#[cfg(test)] +mod tests { + use distributions::Distribution; + use super::{Normal, LogNormal}; + + #[test] + fn test_normal() { + let norm = Normal::new(10.0, 10.0); + let mut rng = ::test::rng(210); + for _ in 0..1000 { + norm.sample(&mut rng); + } + } + #[test] + #[should_panic] + fn test_normal_invalid_sd() { + Normal::new(10.0, -1.0); + } + + + #[test] + fn test_log_normal() { + let lnorm = LogNormal::new(10.0, 10.0); + let mut rng = ::test::rng(211); + for _ in 0..1000 { + lnorm.sample(&mut rng); + } + } + #[test] + #[should_panic] + fn test_log_normal_invalid_sd() { + LogNormal::new(10.0, -1.0); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/other.rs b/crates/rand-0.5.0-pre.2/src/distributions/other.rs new file mode 100644 index 0000000..ef8ce63 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/other.rs @@ -0,0 +1,215 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The implementations of the `Standard` distribution for other built-in types. + +use core::char; +use core::num::Wrapping; + +use {Rng}; +use distributions::{Distribution, Standard, Uniform}; + +// ----- Sampling distributions ----- + +/// Sample a `char`, uniformly distributed over ASCII letters and numbers: +/// a-z, A-Z and 0-9. +/// +/// # Example +/// +/// ``` +/// use std::iter; +/// use rand::{Rng, thread_rng}; +/// use rand::distributions::Alphanumeric; +/// +/// let mut rng = thread_rng(); +/// let chars: String = iter::repeat(()) +/// .map(|()| rng.sample(Alphanumeric)) +/// .take(7) +/// .collect(); +/// println!("Random chars: {}", chars); +/// ``` +#[derive(Debug)] +pub struct Alphanumeric; + + +// ----- Implementations of distributions ----- + +impl Distribution<char> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char { + let range = Uniform::new(0u32, 0x11_0000); + loop { + match char::from_u32(range.sample(rng)) { + Some(c) => return c, + // About 0.2% of numbers in the range 0..0x110000 are invalid + // codepoints (surrogates). + None => {} + } + } + } +} + +impl Distribution<char> for Alphanumeric { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char { + const RANGE: u32 = 26 + 26 + 10; + const GEN_ASCII_STR_CHARSET: &[u8] = + b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\ + abcdefghijklmnopqrstuvwxyz\ + 0123456789"; + // We can pick from 62 characters. This is so close to a power of 2, 64, + // that we can do better than `Uniform`. Use a simple bitshift and + // rejection sampling. We do not use a bitmask, because for small RNGs + // the most significant bits are usually of higher quality. + loop { + let var = rng.next_u32() >> (32 - 6); + if var < RANGE { + return GEN_ASCII_STR_CHARSET[var as usize] as char + } + } + } +} + +impl Distribution<bool> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool { + // We can compare against an arbitrary bit of an u32 to get a bool. + // Because the least significant bits of a lower quality RNG can have + // simple patterns, we compare against the most significant bit. This is + // easiest done using a sign test. + (rng.next_u32() as i32) < 0 + } +} + +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 ),* > + Distribution<( $( $tyvar ),* , )> + for Standard + where $( Standard: Distribution<$tyvar> ),* + { + #[inline] + fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> ( $( $tyvar ),* , ) { + ( + // use the $tyvar's to get the appropriate number of + // repeats (they're not actually needed) + $( + _rng.gen::<$tyvar>() + ),* + , + ) + } + } + } +} + +impl Distribution<()> for Standard { + #[inline] + fn sample<R: Rng + ?Sized>(&self, _: &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 { + // recursive, given at least one type parameter: + {$n:expr, $t:ident, $($ts:ident,)*} => { + array_impl!{($n - 1), $($ts,)*} + + impl<T> Distribution<[T; $n]> for Standard where Standard: Distribution<T> { + #[inline] + fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] { + [_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*] + } + } + }; + // empty case: + {$n:expr,} => { + impl<T> Distribution<[T; $n]> for Standard { + fn sample<R: Rng + ?Sized>(&self, _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> Distribution<Option<T>> for Standard where Standard: Distribution<T> { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Option<T> { + // UFCS is needed here: https://github.com/rust-lang/rust/issues/24066 + if rng.gen::<bool>() { + Some(rng.gen()) + } else { + None + } + } +} + +impl<T> Distribution<Wrapping<T>> for Standard where Standard: Distribution<T> { + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Wrapping<T> { + Wrapping(rng.gen()) + } +} + + +#[cfg(test)] +mod tests { + use {Rng, RngCore, Standard}; + use distributions::Alphanumeric; + #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::String; + + #[test] + fn test_misc() { + let rng: &mut RngCore = &mut ::test::rng(820); + + rng.sample::<char, _>(Standard); + rng.sample::<bool, _>(Standard); + } + + #[cfg(feature="alloc")] + #[test] + fn test_chars() { + use core::iter; + let mut rng = ::test::rng(805); + + // Test by generating a relatively large number of chars, so we also + // take the rejection sampling path. + let word: String = iter::repeat(()) + .map(|()| rng.gen::<char>()).take(1000).collect(); + assert!(word.len() != 0); + } + + #[test] + fn test_alphanumeric() { + let mut rng = ::test::rng(806); + + // Test by generating a relatively large number of chars, so we also + // take the rejection sampling path. + let mut incorrect = false; + for _ in 0..100 { + let c = rng.sample(Alphanumeric); + incorrect |= !((c >= '0' && c <= '9') || + (c >= 'A' && c <= 'Z') || + (c >= 'a' && c <= 'z') ); + } + assert!(incorrect == false); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/poisson.rs b/crates/rand-0.5.0-pre.2/src/distributions/poisson.rs new file mode 100644 index 0000000..8fbf031 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/poisson.rs @@ -0,0 +1,157 @@ +// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The Poisson distribution. + +use Rng; +use distributions::Distribution; +use distributions::log_gamma::log_gamma; +use std::f64::consts::PI; + +/// The Poisson distribution `Poisson(lambda)`. +/// +/// This distribution has a density function: +/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`. +/// +/// # Example +/// +/// ``` +/// use rand::distributions::{Poisson, Distribution}; +/// +/// let poi = Poisson::new(2.0); +/// let v = poi.sample(&mut rand::thread_rng()); +/// println!("{} is from a Poisson(2) distribution", v); +/// ``` +#[derive(Clone, Copy, Debug)] +pub struct Poisson { + lambda: f64, + // precalculated values + exp_lambda: f64, + log_lambda: f64, + sqrt_2lambda: f64, + magic_val: f64, +} + +impl Poisson { + /// Construct a new `Poisson` with the given shape parameter + /// `lambda`. Panics if `lambda <= 0`. + pub fn new(lambda: f64) -> Poisson { + assert!(lambda > 0.0, "Poisson::new called with lambda <= 0"); + let log_lambda = lambda.ln(); + Poisson { + lambda, + exp_lambda: (-lambda).exp(), + log_lambda, + sqrt_2lambda: (2.0 * lambda).sqrt(), + magic_val: lambda * log_lambda - log_gamma(1.0 + lambda), + } + } +} + +impl Distribution<u64> for Poisson { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 { + // using the algorithm from Numerical Recipes in C + + // for low expected values use the Knuth method + if self.lambda < 12.0 { + let mut result = 0; + let mut p = 1.0; + while p > self.exp_lambda { + p *= rng.gen::<f64>(); + result += 1; + } + result - 1 + } + // high expected values - rejection method + else { + let mut int_result: u64; + + loop { + let mut result; + let mut comp_dev; + + // we use the lorentzian distribution as the comparison distribution + // f(x) ~ 1/(1+x/^2) + loop { + // draw from the lorentzian distribution + comp_dev = (PI * rng.gen::<f64>()).tan(); + // shift the peak of the comparison ditribution + result = self.sqrt_2lambda * comp_dev + self.lambda; + // repeat the drawing until we are in the range of possible values + if result >= 0.0 { + break; + } + } + // now the result is a random variable greater than 0 with Lorentzian distribution + // the result should be an integer value + result = result.floor(); + int_result = result as u64; + + // this is the ratio of the Poisson distribution to the comparison distribution + // the magic value scales the distribution function to a range of approximately 0-1 + // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1 + // this doesn't change the resulting distribution, only increases the rate of failed drawings + let check = 0.9 * (1.0 + comp_dev * comp_dev) + * (result * self.log_lambda - log_gamma(1.0 + result) - self.magic_val).exp(); + + // check with uniform random value - if below the threshold, we are within the target distribution + if rng.gen::<f64>() <= check { + break; + } + } + int_result + } + } +} + +#[cfg(test)] +mod test { + use distributions::Distribution; + use super::Poisson; + + #[test] + fn test_poisson_10() { + let poisson = Poisson::new(10.0); + let mut rng = ::test::rng(123); + let mut sum = 0; + for _ in 0..1000 { + sum += poisson.sample(&mut rng); + } + let avg = (sum as f64) / 1000.0; + println!("Poisson average: {}", avg); + assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough + } + + #[test] + fn test_poisson_15() { + // Take the 'high expected values' path + let poisson = Poisson::new(15.0); + let mut rng = ::test::rng(123); + let mut sum = 0; + for _ in 0..1000 { + sum += poisson.sample(&mut rng); + } + let avg = (sum as f64) / 1000.0; + println!("Poisson average: {}", avg); + assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough + } + + #[test] + #[should_panic] + fn test_poisson_invalid_lambda_zero() { + Poisson::new(0.0); + } + + #[test] + #[should_panic] + fn test_poisson_invalid_lambda_neg() { + Poisson::new(-10.0); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/uniform.rs b/crates/rand-0.5.0-pre.2/src/distributions/uniform.rs new file mode 100644 index 0000000..92da829 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/uniform.rs @@ -0,0 +1,867 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! A distribution uniformly sampling numbers within a given range. +//! +//! [`Uniform`] is the standard distribution to sample uniformly from a range; +//! e.g. `Uniform::new_inclusive(1, 6)` can sample integers from 1 to 6, like a +//! standard die. [`Rng::gen_range`] simply uses [`Uniform::sample_single`], +//! thus supports any type supported by [`Uniform`]. +//! +//! This distribution is provided with support for several primitive types +//! (all integer and floating-point types) as well as `std::time::Duration`, +//! and supports extension to user-defined types via a type-specific *back-end* +//! implementation. +//! +//! The types [`UniformInt`], [`UniformFloat`] and [`UniformDuration`] are the +//! back-ends supporting sampling from primitive integer and floating-point +//! ranges as well as from `std::time::Duration`; these types do not normally +//! need to be used directly (unless implementing a derived back-end). +//! +//! # Example usage +//! +//! ``` +//! use rand::{Rng, thread_rng}; +//! use rand::distributions::Uniform; +//! +//! let mut rng = thread_rng(); +//! let side = Uniform::new(-10.0, 10.0); +//! +//! // sample between 1 and 10 points +//! for _ in 0..rng.gen_range(1, 11) { +//! // sample a point from the square with sides -10 - 10 in two dimensions +//! let (x, y) = (rng.sample(side), rng.sample(side)); +//! println!("Point: {}, {}", x, y); +//! } +//! ``` +//! +//! # Extending `Uniform` to support a custom type +//! +//! To extend [`Uniform`] to support your own types, write a back-end which +//! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`] +//! helper trait to "register" your back-end. See the `MyF32` example below. +//! +//! At a minimum, the back-end needs to store any parameters needed for sampling +//! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`. +//! Those methods should include an assert to check the range is valid (i.e. +//! `low < high`). The example below merely wraps another back-end. +//! +//! ``` +//! use rand::prelude::*; +//! use rand::distributions::uniform::{Uniform, SampleUniform, +//! UniformSampler, UniformFloat}; +//! +//! struct MyF32(f32); +//! +//! #[derive(Clone, Copy, Debug)] +//! struct UniformMyF32 { +//! inner: UniformFloat<f32>, +//! } +//! +//! impl UniformSampler for UniformMyF32 { +//! type X = MyF32; +//! fn new(low: Self::X, high: Self::X) -> Self { +//! UniformMyF32 { +//! inner: UniformFloat::<f32>::new(low.0, high.0), +//! } +//! } +//! fn new_inclusive(low: Self::X, high: Self::X) -> Self { +//! UniformSampler::new(low, high) +//! } +//! fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { +//! MyF32(self.inner.sample(rng)) +//! } +//! } +//! +//! impl SampleUniform for MyF32 { +//! type Sampler = UniformMyF32; +//! } +//! +//! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32)); +//! let uniform = Uniform::new(low, high); +//! let x = uniform.sample(&mut thread_rng()); +//! ``` +//! +//! [`Uniform`]: struct.Uniform.html +//! [`Uniform::sample_single`]: struct.Uniform.html#method.sample_single +//! [`Rng::gen_range`]: ../../trait.Rng.html#method.gen_range +//! [`SampleUniform`]: trait.SampleUniform.html +//! [`UniformSampler`]: trait.UniformSampler.html +//! [`UniformInt`]: struct.UniformInt.html +//! [`UniformFloat`]: struct.UniformFloat.html +//! [`UniformDuration`]: struct.UniformDuration.html + +#[cfg(feature = "std")] +use std::time::Duration; + +use Rng; +use distributions::Distribution; +use distributions::float::IntoFloat; + +/// Sample values uniformly between two bounds. +/// +/// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform +/// distribution sampling from the given range; these functions may do extra +/// work up front to make sampling of multiple values faster. +/// +/// [`Uniform::sample_single`] instead samples directly from the given range, +/// and (depending on the back-end) may be faster when sampling a very small +/// number of values or only a single value from this range. +/// +/// When sampling from a constant range, many calculations can happen at +/// compile-time and all methods should be fast; for floating-point ranges and +/// the full range of integer types this should have comparable performance to +/// the `Standard` distribution. +/// +/// Steps are taken to avoid bias which might be present in naive +/// implementations; for example `rng.gen::<u8>() % 170` samples from the range +/// `[0, 169]` but is twice as likely to select numbers less than 85 than other +/// values. Further, the implementations here give more weight to the high-bits +/// generated by the RNG than the low bits, since with some RNGs the low-bits +/// are of lower quality than the high bits. +/// +/// Implementations should attempt to sample in `[low, high)` for +/// `Uniform::new(low, high)`, i.e., excluding `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 +/// +/// ``` +/// use rand::distributions::{Distribution, Uniform}; +/// +/// fn main() { +/// let between = Uniform::from(10..10000); +/// let mut rng = rand::thread_rng(); +/// let mut sum = 0; +/// for _ in 0..1000 { +/// sum += between.sample(&mut rng); +/// } +/// println!("{}", sum); +/// } +/// ``` +/// +/// [`Uniform::new`]: struct.Uniform.html#method.new +/// [`Uniform::new_inclusive`]: struct.Uniform.html#method.new_inclusive +/// [`Uniform::sample_single`]: struct.Uniform.html#method.sample_single +/// [`new`]: struct.Uniform.html#method.new +/// [`new_inclusive`]: struct.Uniform.html#method.new_inclusive +/// [`sample_single`]: struct.Uniform.html#method.sample_single +#[derive(Clone, Copy, Debug)] +pub struct Uniform<X: SampleUniform> { + inner: X::Sampler, +} + +impl<X: SampleUniform> Uniform<X> { + /// Create a new `Uniform` instance which samples uniformly from the half + /// open range `[low, high)` (excluding `high`). Panics if `low >= high`. + pub fn new(low: X, high: X) -> Uniform<X> { + Uniform { inner: X::Sampler::new(low, high) } + } + + /// Create a new `Uniform` instance which samples uniformly from the closed + /// range `[low, high]` (inclusive). Panics if `low > high`. + pub fn new_inclusive(low: X, high: X) -> Uniform<X> { + Uniform { inner: X::Sampler::new_inclusive(low, high) } + } + + /// Sample a single value uniformly from `[low, high)`. + /// Panics if `low >= high`. + pub fn sample_single<R: Rng + ?Sized>(low: X, high: X, rng: &mut R) -> X { + X::Sampler::sample_single(low, high, rng) + } +} + +impl<X: SampleUniform> Distribution<X> for Uniform<X> { + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X { + self.inner.sample(rng) + } +} + +/// Helper trait for creating objects using the correct implementation of +/// [`UniformSampler`] for the sampling type. +/// +/// See the [module documentation] on how to implement [`Uniform`] range +/// sampling for a custom type. +/// +/// [`UniformSampler`]: trait.UniformSampler.html +/// [module documentation]: index.html +/// [`Uniform`]: struct.Uniform.html +pub trait SampleUniform: Sized { + /// The `UniformSampler` implementation supporting type `X`. + type Sampler: UniformSampler<X = Self>; +} + +/// Helper trait handling actual uniform sampling. +/// +/// See the [module documentation] on how to implement [`Uniform`] range +/// sampling for a custom type. +/// +/// Implementation of [`sample_single`] is optional, and is only useful when +/// the implementation can be faster than `Self::new(low, high).sample(rng)`. +/// +/// [module documentation]: index.html +/// [`Uniform`]: struct.Uniform.html +/// [`sample_single`]: trait.UniformSampler.html#method.sample_single +pub trait UniformSampler: Sized { + /// The type sampled by this implementation. + type X; + + /// Construct self, with inclusive lower bound and exclusive upper bound + /// `[low, high)`. + /// + /// Usually users should not call this directly but instead use + /// `Uniform::new`, which asserts that `low < high` before calling this. + fn new(low: Self::X, high: Self::X) -> Self; + + /// Construct self, with inclusive bounds `[low, high]`. + /// + /// Usually users should not call this directly but instead use + /// `Uniform::new_inclusive`, which asserts that `low <= high` before + /// calling this. + fn new_inclusive(low: Self::X, high: Self::X) -> Self; + + /// Sample a value. + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X; + + /// Sample a single value uniformly from a range with inclusive lower bound + /// and exclusive upper bound `[low, high)`. + /// + /// Usually users should not call this directly but instead use + /// `Uniform::sample_single`, which asserts that `low < high` before calling + /// this. + /// + /// Via this method, implementations can provide a method optimized for + /// sampling only a single value from the specified range. The default + /// implementation simply calls `UniformSampler::new` then `sample` on the + /// result. + fn sample_single<R: Rng + ?Sized>(low: Self::X, high: Self::X, rng: &mut R) + -> Self::X + { + let uniform: Self = UniformSampler::new(low, high); + uniform.sample(rng) + } +} + +impl<X: SampleUniform> From<::core::ops::Range<X>> for Uniform<X> { + fn from(r: ::core::ops::Range<X>) -> Uniform<X> { + Uniform::new(r.start, r.end) + } +} + +//////////////////////////////////////////////////////////////////////////////// + +// What follows are all back-ends. + + +/// The back-end implementing [`UniformSampler`] for integer types. +/// +/// Unless you are implementing [`UniformSampler`] for your own type, this type +/// should not be used directly, use [`Uniform`] instead. +/// +/// # Implementation notes +/// +/// For a closed range, the number of possible numbers we should generate is +/// `range = (high - low + 1)`. It is not possible to end up with a uniform +/// distribution if we map *all* the random integers that can be generated to +/// this range. We have to map integers from a `zone` that is a multiple of the +/// range. The rest of the integers, that cause a bias, are rejected. +/// +/// The problem with `range` is that to cover the full range of the type, it has +/// to store `unsigned_max + 1`, which can't be represented. But if the range +/// covers the full range of the type, no modulus is needed. A range of size 0 +/// can't exist, so we use that to represent this special case. Wrapping +/// arithmetic even makes representing `unsigned_max + 1` as 0 simple. +/// +/// We don't calculate `zone` directly, but first calculate the number of +/// integers to reject. To handle `unsigned_max + 1` not fitting in the type, +/// we use: +/// `ints_to_reject = (unsigned_max + 1) % range;` +/// `ints_to_reject = (unsigned_max - range + 1) % range;` +/// +/// The smallest integer PRNGs generate is `u32`. That is why for small integer +/// sizes (`i8`/`u8` and `i16`/`u16`) there is an optimization: don't pick the +/// largest zone that can fit in the small type, but pick the largest zone that +/// can fit in an `u32`. `ints_to_reject` is always less than half the size of +/// the small integer. This means the first bit of `zone` is always 1, and so +/// are all the other preceding bits of a larger integer. The easiest way to +/// grow the `zone` for the larger type is to simply sign extend it. +/// +/// An alternative to using a modulus is widening multiply: After a widening +/// multiply by `range`, the result is in the high word. Then comparing the low +/// word against `zone` makes sure our distribution is uniform. +/// +/// [`UniformSampler`]: trait.UniformSampler.html +/// [`Uniform`]: struct.Uniform.html +#[derive(Clone, Copy, Debug)] +pub struct UniformInt<X> { + low: X, + range: X, + zone: X, +} + +macro_rules! uniform_int_impl { + ($ty:ty, $signed:ty, $unsigned:ident, + $i_large:ident, $u_large:ident) => { + impl SampleUniform for $ty { + type Sampler = UniformInt<$ty>; + } + + impl UniformSampler for UniformInt<$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. + + type X = $ty; + + #[inline] // if the range is constant, this helps LLVM to do the + // calculations at compile-time. + fn new(low: Self::X, high: Self::X) -> Self { + assert!(low < high, "Uniform::new called with `low >= high`"); + UniformSampler::new_inclusive(low, high - 1) + } + + #[inline] // if the range is constant, this helps LLVM to do the + // calculations at compile-time. + fn new_inclusive(low: Self::X, high: Self::X) -> Self { + assert!(low <= high, + "Uniform::new_inclusive called with `low > high`"); + let unsigned_max = ::core::$unsigned::MAX; + + let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned; + let ints_to_reject = + if range > 0 { + (unsigned_max - range + 1) % range + } else { + 0 + }; + let zone = unsigned_max - ints_to_reject; + + UniformInt { + low: low, + // These are really $unsigned values, but store as $ty: + range: range as $ty, + zone: zone as $ty + } + } + + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { + let range = self.range as $unsigned as $u_large; + if range > 0 { + // Grow `zone` to fit a type of at least 32 bits, by + // sign-extending it (the first bit is always 1, so are all + // the preceding bits of the larger type). + // For types that already have the right size, all the + // casting is a no-op. + let zone = self.zone as $signed as $i_large as $u_large; + loop { + let v: $u_large = rng.gen(); + let (hi, lo) = v.wmul(range); + if lo <= zone { + return self.low.wrapping_add(hi as $ty); + } + } + } else { + // Sample from the entire integer range. + rng.gen() + } + } + + fn sample_single<R: Rng + ?Sized>(low: Self::X, + high: Self::X, + rng: &mut R) -> Self::X + { + assert!(low < high, + "Uniform::sample_single called with low >= high"); + let range = high.wrapping_sub(low) as $unsigned as $u_large; + let zone = + if ::core::$unsigned::MAX <= ::core::u16::MAX as $unsigned { + // Using a modulus is faster than the approximation for + // i8 and i16. I suppose we trade the cost of one + // modulus for near-perfect branch prediction. + let unsigned_max: $u_large = ::core::$u_large::MAX; + let ints_to_reject = (unsigned_max - range + 1) % range; + unsigned_max - ints_to_reject + } else { + // conservative but fast approximation + range << range.leading_zeros() + }; + + loop { + let v: $u_large = rng.gen(); + let (hi, lo) = v.wmul(range); + if lo <= zone { + return low.wrapping_add(hi as $ty); + } + } + } + } + } +} + +uniform_int_impl! { i8, i8, u8, i32, u32 } +uniform_int_impl! { i16, i16, u16, i32, u32 } +uniform_int_impl! { i32, i32, u32, i32, u32 } +uniform_int_impl! { i64, i64, u64, i64, u64 } +#[cfg(feature = "i128_support")] +uniform_int_impl! { i128, i128, u128, u128, u128 } +uniform_int_impl! { isize, isize, usize, isize, usize } +uniform_int_impl! { u8, i8, u8, i32, u32 } +uniform_int_impl! { u16, i16, u16, i32, u32 } +uniform_int_impl! { u32, i32, u32, i32, u32 } +uniform_int_impl! { u64, i64, u64, i64, u64 } +uniform_int_impl! { usize, isize, usize, isize, usize } +#[cfg(feature = "i128_support")] +uniform_int_impl! { u128, u128, u128, i128, u128 } + + +trait WideningMultiply<RHS = Self> { + type Output; + + fn wmul(self, x: RHS) -> Self::Output; +} + +macro_rules! wmul_impl { + ($ty:ty, $wide:ty, $shift:expr) => { + impl WideningMultiply for $ty { + type Output = ($ty, $ty); + + #[inline(always)] + fn wmul(self, x: $ty) -> Self::Output { + let tmp = (self as $wide) * (x as $wide); + ((tmp >> $shift) as $ty, tmp as $ty) + } + } + } +} +wmul_impl! { u8, u16, 8 } +wmul_impl! { u16, u32, 16 } +wmul_impl! { u32, u64, 32 } +#[cfg(feature = "i128_support")] +wmul_impl! { u64, u128, 64 } + +// This code is a translation of the __mulddi3 function in LLVM's +// compiler-rt. It is an optimised variant of the common method +// `(a + b) * (c + d) = ac + ad + bc + bd`. +// +// For some reason LLVM can optimise the C version very well, but +// keeps shuffeling registers in this Rust translation. +macro_rules! wmul_impl_large { + ($ty:ty, $half:expr) => { + impl WideningMultiply for $ty { + type Output = ($ty, $ty); + + #[inline(always)] + fn wmul(self, b: $ty) -> Self::Output { + const LOWER_MASK: $ty = !0 >> $half; + let mut low = (self & LOWER_MASK).wrapping_mul(b & LOWER_MASK); + let mut t = low >> $half; + low &= LOWER_MASK; + t += (self >> $half).wrapping_mul(b & LOWER_MASK); + low += (t & LOWER_MASK) << $half; + let mut high = t >> $half; + t = low >> $half; + low &= LOWER_MASK; + t += (b >> $half).wrapping_mul(self & LOWER_MASK); + low += (t & LOWER_MASK) << $half; + high += t >> $half; + high += (self >> $half).wrapping_mul(b >> $half); + + (high, low) + } + } + } +} +#[cfg(not(feature = "i128_support"))] +wmul_impl_large! { u64, 32 } +#[cfg(feature = "i128_support")] +wmul_impl_large! { u128, 64 } + +macro_rules! wmul_impl_usize { + ($ty:ty) => { + impl WideningMultiply for usize { + type Output = (usize, usize); + + #[inline(always)] + fn wmul(self, x: usize) -> Self::Output { + let (high, low) = (self as $ty).wmul(x as $ty); + (high as usize, low as usize) + } + } + } +} +#[cfg(target_pointer_width = "32")] +wmul_impl_usize! { u32 } +#[cfg(target_pointer_width = "64")] +wmul_impl_usize! { u64 } + + + +/// The back-end implementing [`UniformSampler`] for floating-point types. +/// +/// Unless you are implementing [`UniformSampler`] for your own type, this type +/// should not be used directly, use [`Uniform`] instead. +/// +/// # Implementation notes +/// +/// Instead of generating a float in the `[0, 1)` range using [`Standard`], the +/// `UniformFloat` implementation converts the output of an PRNG itself. This +/// way one or two steps can be optimized out. +/// +/// The floats are first converted to a value in the `[1, 2)` interval using a +/// transmute-based method, and then mapped to the expected range with a +/// multiply and addition. Values produced this way have what equals 22 bits of +/// random digits for an `f32`, and 52 for an `f64`. +/// +/// Currently there is no difference between [`new`] and [`new_inclusive`], +/// because the boundaries of a floats range are a bit of a fuzzy concept due to +/// rounding errors. +/// +/// [`UniformSampler`]: trait.UniformSampler.html +/// [`new`]: trait.UniformSampler.html#tymethod.new +/// [`new_inclusive`]: trait.UniformSampler.html#tymethod.new_inclusive +/// [`Uniform`]: struct.Uniform.html +/// [`Standard`]: ../struct.Standard.html +#[derive(Clone, Copy, Debug)] +pub struct UniformFloat<X> { + scale: X, + offset: X, +} + +macro_rules! uniform_float_impl { + ($ty:ty, $bits_to_discard:expr, $next_u:ident) => { + impl SampleUniform for $ty { + type Sampler = UniformFloat<$ty>; + } + + impl UniformSampler for UniformFloat<$ty> { + type X = $ty; + + fn new(low: Self::X, high: Self::X) -> Self { + assert!(low < high, "Uniform::new called with `low >= high`"); + let scale = high - low; + let offset = low - scale; + UniformFloat { + scale: scale, + offset: offset, + } + } + + fn new_inclusive(low: Self::X, high: Self::X) -> Self { + assert!(low <= high, + "Uniform::new_inclusive called with `low > high`"); + let scale = high - low; + let offset = low - scale; + UniformFloat { + scale: scale, + offset: offset, + } + } + + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { + // Generate a value in the range [1, 2) + let value1_2 = (rng.$next_u() >> $bits_to_discard) + .into_float_with_exponent(0); + // We don't use `f64::mul_add`, because it is not available with + // `no_std`. Furthermore, it is slower for some targets (but + // faster for others). However, the order of multiplication and + // addition is important, because on some platforms (e.g. ARM) + // it will be optimized to a single (non-FMA) instruction. + value1_2 * self.scale + self.offset + } + + fn sample_single<R: Rng + ?Sized>(low: Self::X, + high: Self::X, + rng: &mut R) -> Self::X { + assert!(low < high, + "Uniform::sample_single called with low >= high"); + let scale = high - low; + let offset = low - scale; + // Generate a value in the range [1, 2) + let value1_2 = (rng.$next_u() >> $bits_to_discard) + .into_float_with_exponent(0); + // Doing multiply before addition allows some architectures to + // use a single instruction. + value1_2 * scale + offset + } + } + } +} + +uniform_float_impl! { f32, 32 - 23, next_u32 } +uniform_float_impl! { f64, 64 - 52, next_u64 } + + + +/// The back-end implementing [`UniformSampler`] for `Duration`. +/// +/// Unless you are implementing [`UniformSampler`] for your own types, this type +/// should not be used directly, use [`Uniform`] instead. +/// +/// [`UniformSampler`]: trait.UniformSampler.html +/// [`Uniform`]: struct.Uniform.html +#[cfg(feature = "std")] +#[derive(Clone, Copy, Debug)] +pub struct UniformDuration { + offset: Duration, + mode: UniformDurationMode, +} + +#[cfg(feature = "std")] +#[derive(Debug, Copy, Clone)] +enum UniformDurationMode { + Small { + nanos: Uniform<u64>, + }, + Large { + size: Duration, + secs: Uniform<u64>, + } +} + +#[cfg(feature = "std")] +impl SampleUniform for Duration { + type Sampler = UniformDuration; +} + +#[cfg(feature = "std")] +impl UniformSampler for UniformDuration { + type X = Duration; + + #[inline] + fn new(low: Duration, high: Duration) -> UniformDuration { + assert!(low < high, "Uniform::new called with `low >= high`"); + UniformDuration::new_inclusive(low, high - Duration::new(0, 1)) + } + + #[inline] + fn new_inclusive(low: Duration, high: Duration) -> UniformDuration { + assert!(low <= high, "Uniform::new_inclusive called with `low > high`"); + let size = high - low; + let nanos = size + .as_secs() + .checked_mul(1_000_000_000) + .and_then(|n| n.checked_add(size.subsec_nanos() as u64)); + + let mode = match nanos { + Some(nanos) => { + UniformDurationMode::Small { + nanos: Uniform::new_inclusive(0, nanos), + } + } + None => { + UniformDurationMode::Large { + size: size, + secs: Uniform::new_inclusive(0, size.as_secs()), + } + } + }; + + UniformDuration { + mode, + offset: low, + } + } + + #[inline] + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Duration { + let d = match self.mode { + UniformDurationMode::Small { nanos } => { + let nanos = nanos.sample(rng); + Duration::new(nanos / 1_000_000_000, (nanos % 1_000_000_000) as u32) + } + UniformDurationMode::Large { size, secs } => { + loop { + let d = Duration::new(secs.sample(rng), rng.gen_range(0, 1_000_000_000)); + if d <= size { + break d; + } + } + } + }; + + self.offset + d + } +} + +#[cfg(test)] +mod tests { + use Rng; + use distributions::uniform::{Uniform, UniformSampler, UniformFloat, SampleUniform}; + + #[should_panic] + #[test] + fn test_uniform_bad_limits_equal_int() { + Uniform::new(10, 10); + } + + #[should_panic] + #[test] + fn test_uniform_bad_limits_equal_float() { + Uniform::new(10., 10.); + } + + #[test] + fn test_uniform_good_limits_equal_int() { + let mut rng = ::test::rng(804); + let dist = Uniform::new_inclusive(10, 10); + for _ in 0..20 { + assert_eq!(rng.sample(dist), 10); + } + } + + #[test] + fn test_uniform_good_limits_equal_float() { + let mut rng = ::test::rng(805); + let dist = Uniform::new_inclusive(10., 10.); + for _ in 0..20 { + assert_eq!(rng.sample(dist), 10.); + } + } + + #[should_panic] + #[test] + fn test_uniform_bad_limits_flipped_int() { + Uniform::new(10, 5); + } + + #[should_panic] + #[test] + fn test_uniform_bad_limits_flipped_float() { + Uniform::new(10., 5.); + } + + #[test] + fn test_integers() { + let mut rng = ::test::rng(251); + 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 my_uniform = Uniform::new(low, high); + for _ in 0..1000 { + let v: $ty = rng.sample(my_uniform); + assert!(low <= v && v < high); + } + + let my_uniform = Uniform::new_inclusive(low, high); + for _ in 0..1000 { + let v: $ty = rng.sample(my_uniform); + assert!(low <= v && v <= high); + } + + for _ in 0..1000 { + let v: $ty = Uniform::sample_single(low, high, &mut rng); + assert!(low <= v && v < high); + } + } + )* + }} + } + t!(i8, i16, i32, i64, isize, + u8, u16, u32, u64, usize); + #[cfg(feature = "i128_support")] + t!(i128, u128) + } + + #[test] + fn test_floats() { + let mut rng = ::test::rng(252); + macro_rules! t { + ($($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 my_uniform = Uniform::new(low, high); + for _ in 0..1000 { + let v: $ty = rng.sample(my_uniform); + assert!(low <= v && v < high); + } + } + )* + }} + } + + t!(f32, f64) + } + + #[test] + #[cfg(feature = "std")] + fn test_durations() { + use std::time::Duration; + + let mut rng = ::test::rng(253); + + let v = &[(Duration::new(10, 50000), Duration::new(100, 1234)), + (Duration::new(0, 100), Duration::new(1, 50)), + (Duration::new(0, 0), Duration::new(u64::max_value(), 999_999_999))]; + for &(low, high) in v.iter() { + let my_uniform = Uniform::new(low, high); + for _ in 0..1000 { + let v = rng.sample(my_uniform); + assert!(low <= v && v < high); + } + } + } + + #[test] + fn test_custom_uniform() { + #[derive(Clone, Copy, PartialEq, PartialOrd)] + struct MyF32 { + x: f32, + } + #[derive(Clone, Copy, Debug)] + struct UniformMyF32 { + inner: UniformFloat<f32>, + } + impl UniformSampler for UniformMyF32 { + type X = MyF32; + fn new(low: Self::X, high: Self::X) -> Self { + UniformMyF32 { + inner: UniformFloat::<f32>::new(low.x, high.x), + } + } + fn new_inclusive(low: Self::X, high: Self::X) -> Self { + UniformSampler::new(low, high) + } + fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { + MyF32 { x: self.inner.sample(rng) } + } + } + impl SampleUniform for MyF32 { + type Sampler = UniformMyF32; + } + + let (low, high) = (MyF32{ x: 17.0f32 }, MyF32{ x: 22.0f32 }); + let uniform = Uniform::new(low, high); + let mut rng = ::test::rng(804); + for _ in 0..100 { + let x: MyF32 = rng.sample(uniform); + assert!(low <= x && x < high); + } + } + + #[test] + fn test_uniform_from_std_range() { + let r = Uniform::from(2u32..7); + assert_eq!(r.inner.low, 2); + assert_eq!(r.inner.range, 5); + let r = Uniform::from(2.0f64..7.0); + assert_eq!(r.inner.offset, -3.0); + assert_eq!(r.inner.scale, 5.0); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/distributions/ziggurat_tables.rs b/crates/rand-0.5.0-pre.2/src/distributions/ziggurat_tables.rs new file mode 100644 index 0000000..11a2172 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/distributions/ziggurat_tables.rs @@ -0,0 +1,280 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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/crates/rand-0.5.0-pre.2/src/lib.rs b/crates/rand-0.5.0-pre.2/src/lib.rs new file mode 100644 index 0000000..a47a107 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/lib.rs @@ -0,0 +1,1189 @@ +// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Utilities for random number generation +//! +//! Rand provides utilities to generate random numbers, to convert them to +//! useful types and distributions, and some randomness-related algorithms. +//! +//! # Basic usage +//! +//! To get you started quickly, the easiest and highest-level way to get +//! a random value is to use [`random()`]. +//! +//! ``` +//! let x: u8 = rand::random(); +//! println!("{}", x); +//! +//! let y = rand::random::<f64>(); +//! println!("{}", y); +//! +//! if rand::random() { // generates a boolean +//! println!("Heads!"); +//! } +//! ``` +//! +//! This supports generating most common types but is not very flexible, thus +//! you probably want to learn a bit more about the Rand library. +//! +//! +//! # The two-step process to get a random value +//! +//! Generating random values is typically a two-step process: +//! +//! - get some *random data* (an integer or bit/byte sequence) from a random +//! number generator (RNG); +//! - use some function to transform that *data* into the type of value you want +//! (this function is an implementation of some *distribution* describing the +//! kind of value produced). +//! +//! Rand represents the first step with the [`RngCore`] trait and the second +//! step via a combination of the [`Rng`] extension trait and the +//! [`distributions` module]. +//! In practice you probably won't use [`RngCore`] directly unless you are +//! implementing a random number generator (RNG). +//! +//! There are many kinds of RNGs, with different trade-offs. You can read more +//! about them in the [`rngs` module] and even more in the [`prng` module], +//! however, often you can just use [`thread_rng()`]. This function +//! automatically initializes an RNG in thread-local memory, then returns a +//! reference to it. It is fast, good quality, and secure (unpredictable). +//! +//! To turn the output of the RNG into something usable, you usually want to use +//! the methods from the [`Rng`] trait. Some of the most useful methods are: +//! +//! - [`gen`] generates a random value appropriate for the type (just like +//! [`random()`]). For integers this is normally the full representable range +//! (e.g. from `0u32` to `std::u32::MAX`), for floats this is between 0 and 1, +//! and some other types are supported, including arrays and tuples. See the +//! [`Standard`] distribution which provides the implementations. +//! - [`gen_range`] samples from a specific range of values; this is like +//! [`gen`] but with specific upper and lower bounds. +//! - [`sample`] samples directly from some distribution. +//! +//! [`random()`] is defined using just the above: `thread_rng().gen()`. +//! +//! ## Distributions +//! +//! What are distributions, you ask? Specifying only the type and range of +//! values (known as the *sample space*) is not enough; samples must also have +//! a *probability distribution*, describing the relative probability of +//! sampling each value in that space. +//! +//! In many cases a *uniform* distribution is used, meaning roughly that each +//! value is equally likely (or for "continuous" types like floats, that each +//! equal-sized sub-range has the same probability of containing a sample). +//! [`gen`] and [`gen_range`] both use statistically uniform distributions. +//! +//! The [`distributions` module] provides implementations +//! of some other distributions, including Normal, Log-Normal and Exponential. +//! +//! It is worth noting that the functionality already mentioned is implemented +//! with distributions: [`gen`] samples values using the [`Standard`] +//! distribution, while [`gen_range`] uses [`Uniform`]. +//! +//! ## Importing (prelude) +//! +//! The most convenient way to import items from Rand is to use the [prelude]. +//! This includes the most important parts of Rand, but only those unlikely to +//! cause name conflicts. +//! +//! Note that Rand 0.5 has significantly changed the module organization and +//! contents relative to previous versions. Where possible old names have been +//! kept (but are hidden in the documentation), however these will be removed +//! in the future. We therefore recommend migrating to use the prelude or the +//! new module organization in your imports. +//! +//! +//! ## Examples +//! +//! ``` +//! use rand::prelude::*; +//! +//! // thread_rng is often the most convenient source of randomness: +//! let mut rng = thread_rng(); +//! +//! if rng.gen() { // random bool +//! let x: f64 = rng.gen(); // random number in range (0, 1) +//! println!("x is: {}", x); +//! let char = rng.gen::<char>(); // using type annotation +//! println!("char is: {}", char); +//! println!("Number from 0 to 9: {}", rng.gen_range(0, 10)); +//! } +//! ``` +//! +//! +//! # More functionality +//! +//! The [`Rng`] trait includes a few more methods not mentioned above: +//! +//! - [`Rng::sample_iter`] allows iterating over values from a chosen +//! distribution. +//! - [`Rng::gen_bool`] generates boolean "events" with a given probability. +//! - [`Rng::fill`] and [`Rng::try_fill`] are fast alternatives to fill a slice +//! of integers. +//! - [`Rng::shuffle`] randomly shuffles elements in a slice. +//! - [`Rng::choose`] picks one element at random from a slice. +//! +//! For more slice/sequence related functionality, look in the [`seq` module]. +//! +//! There is also [`distributions::WeightedChoice`], which can be used to pick +//! elements at random with some probability. But it does not work well at the +//! moment and is going through a redesign. +//! +//! +//! # Error handling +//! +//! Error handling in Rand is a compromise between simplicity and necessity. +//! Most RNGs and sampling functions will never produce errors, and making these +//! able to handle errors would add significant overhead (to code complexity +//! and ergonomics of usage at least, and potentially also performance, +//! depending on the approach). +//! However, external RNGs can fail, and being able to handle this is important. +//! +//! It has therefore been decided that *most* methods should not return a +//! `Result` type, with as exceptions [`Rng::try_fill`], +//! [`RngCore::try_fill_bytes`], and [`SeedableRng::from_rng`]. +//! +//! Note that it is the RNG that panics when it fails but is not used through a +//! method that can report errors. Currently Rand contains only three RNGs that +//! can return an error (and thus may panic), and documents this property: +//! [`OsRng`], [`EntropyRng`] and [`ReadRng`]. Other RNGs, like [`ThreadRng`] +//! and [`StdRng`], can be used with all methods without concern. +//! +//! One further problem is that if Rand is unable to get any external randomness +//! when initializing an RNG with [`EntropyRng`], it will panic in +//! [`FromEntropy::from_entropy`], and notably in [`thread_rng`]. Except by +//! compromising security, this problem is as unsolvable as running out of +//! memory. +//! +//! +//! # Distinction between Rand and `rand_core` +//! +//! The [`rand_core`] crate provides the necessary traits and functionality for +//! implementing RNGs; this includes the [`RngCore`] and [`SeedableRng`] traits +//! and the [`Error`] type. +//! Crates implementing RNGs should depend on [`rand_core`]. +//! +//! Applications and libraries consuming random values are encouraged to use the +//! Rand crate, which re-exports the common parts of [`rand_core`]. +//! +//! +//! # More examples +//! +//! For some inspiration, see the examples: +//! +//! - [Monte Carlo estimation of π]( +//! https://github.com/rust-lang-nursery/rand/blob/master/examples/monte-carlo.r...) +//! - [Monty Hall Problem]( +//! https://github.com/rust-lang-nursery/rand/blob/master/examples/monty-hall.rs) +//! +//! +//! [`distributions` module]: distributions/index.html +//! [`distributions::WeightedChoice`]: distributions/struct.WeightedChoice.html +//! [`EntropyRng`]: rngs/struct.EntropyRng.html +//! [`Error`]: struct.Error.html +//! [`gen_range`]: trait.Rng.html#method.gen_range +//! [`gen`]: trait.Rng.html#method.gen +//! [`OsRng`]: rngs/struct.OsRng.html +//! [prelude]: prelude/index.html +//! [`rand_core`]: https://crates.io/crates/rand_core +//! [`random()`]: fn.random.html +//! [`ReadRng`]: rngs/adapter/struct.ReadRng.html +//! [`Rng::choose`]: trait.Rng.html#method.choose +//! [`Rng::fill`]: trait.Rng.html#method.fill +//! [`Rng::gen_bool`]: trait.Rng.html#method.gen_bool +//! [`Rng::gen`]: trait.Rng.html#method.gen +//! [`Rng::sample_iter`]: trait.Rng.html#method.sample_iter +//! [`Rng::shuffle`]: trait.Rng.html#method.shuffle +//! [`RngCore`]: trait.RngCore.html +//! [`RngCore::try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes +//! [`rngs` module]: rngs/index.html +//! [`prng` module]: prng/index.html +//! [`Rng`]: trait.Rng.html +//! [`Rng::try_fill`]: trait.Rng.html#method.try_fill +//! [`sample`]: trait.Rng.html#method.sample +//! [`SeedableRng`]: trait.SeedableRng.html +//! [`SeedableRng::from_rng`]: trait.SeedableRng.html#method.from_rng +//! [`seq` module]: seq/index.html +//! [`SmallRng`]: rngs/struct.SmallRng.html +//! [`StdRng`]: rngs/struct.StdRng.html +//! [`thread_rng()`]: fn.thread_rng.html +//! [`ThreadRng`]: rngs/struct.ThreadRng.html +//! [`Standard`]: distributions/struct.Standard.html +//! [`Uniform`]: distributions/struct.Uniform.html + + +#![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/0.5")] + +#![deny(missing_docs)] +#![deny(missing_debug_implementations)] +#![doc(test(attr(allow(unused_variables), deny(warnings))))] + +#![cfg_attr(not(feature="std"), no_std)] +#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))] +#![cfg_attr(all(feature="i128_support", feature="nightly"), allow(stable_features))] // stable since 2018-03-27 +#![cfg_attr(all(feature="i128_support", feature="nightly"), feature(i128_type, i128))] +#![cfg_attr(feature = "stdweb", recursion_limit="128")] + +#[cfg(feature="std")] extern crate std as core; +#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc; + +#[cfg(test)] #[cfg(feature="serde1")] extern crate bincode; +#[cfg(feature="serde1")] extern crate serde; +#[cfg(feature="serde1")] #[macro_use] extern crate serde_derive; + +#[cfg(all(target_arch="wasm32", not(target_os="emscripten"), feature="stdweb"))] +#[macro_use] +extern crate stdweb; + +extern crate rand_core; + +#[cfg(feature = "log")] #[macro_use] extern crate log; +#[cfg(not(feature = "log"))] macro_rules! trace { ($($x:tt)*) => () } +#[cfg(not(feature = "log"))] macro_rules! debug { ($($x:tt)*) => () } +#[cfg(all(feature="std", not(feature = "log")))] macro_rules! info { ($($x:tt)*) => () } +#[cfg(not(feature = "log"))] macro_rules! warn { ($($x:tt)*) => () } +#[cfg(all(feature="std", not(feature = "log")))] macro_rules! error { ($($x:tt)*) => () } + + +// Re-exports from rand_core +pub use rand_core::{RngCore, CryptoRng, SeedableRng}; +pub use rand_core::{ErrorKind, Error}; + +// Public exports +#[cfg(feature="std")] pub use rngs::thread::thread_rng; + +// Public modules +pub mod distributions; +pub mod prelude; +pub mod prng; +pub mod rngs; +#[cfg(feature = "alloc")] pub mod seq; + +//////////////////////////////////////////////////////////////////////////////// +// Compatibility re-exports. Documentation is hidden; will be removed eventually. + +#[cfg(feature="std")] #[doc(hidden)] pub use rngs::adapter::read; +#[doc(hidden)] pub use rngs::adapter::ReseedingRng; + +#[doc(hidden)] pub use rngs::jitter; +#[cfg(feature="std")] #[doc(hidden)] pub use rngs::{os, EntropyRng, OsRng}; + +#[doc(hidden)] pub use prng::{ChaChaRng, IsaacRng, Isaac64Rng, XorShiftRng}; +#[doc(hidden)] pub use rngs::StdRng; + + +#[doc(hidden)] +pub mod chacha { + //! The ChaCha random number generator. + pub use prng::ChaChaRng; +} +#[doc(hidden)] +pub mod isaac { + //! The ISAAC random number generator. + pub use prng::{IsaacRng, Isaac64Rng}; +} + +#[cfg(feature="std")] #[doc(hidden)] pub use rngs::ThreadRng; + +//////////////////////////////////////////////////////////////////////////////// + + +use core::{marker, mem, slice}; +use distributions::{Distribution, Standard, Uniform}; +use distributions::uniform::SampleUniform; + + +/// A type that can be randomly generated using an [`Rng`]. +/// +/// This is merely an adapter around the [`Standard`] distribution for +/// convenience and backwards-compatibility. +/// +/// [`Rng`]: trait.Rng.html +/// [`Standard`]: distributions/struct.Standard.html +#[deprecated(since="0.5.0", note="replaced by distributions::Standard")] +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; +} + +/// An automatically-implemented extension trait on [`RngCore`] providing high-level +/// generic methods for sampling values and other convenience methods. +/// +/// This is the primary trait to use when generating random values. +/// +/// # Generic usage +/// +/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some +/// things are worth noting here: +/// +/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no +/// difference whether we use `R: Rng` or `R: RngCore`. +/// - The `+ ?Sized` un-bounding allows functions to be called directly on +/// type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without +/// this it would be necessary to write `foo(&mut r)`. +/// +/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some +/// trade-offs. It allows the argument to be consumed directly without a `&mut` +/// (which is how `from_rng(thread_rng())` works); also it still works directly +/// on references (including type-erased references). Unfortunately within the +/// function `foo` it is not known whether `rng` is a reference type or not, +/// hence many uses of `rng` require an extra reference, either explicitly +/// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the +/// optimiser can remove redundant references later. +/// +/// Example: +/// +/// ``` +/// # use rand::thread_rng; +/// use rand::Rng; +/// +/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 { +/// rng.gen() +/// } +/// +/// # let v = foo(&mut thread_rng()); +/// ``` +/// +/// [`RngCore`]: trait.RngCore.html +pub trait Rng: RngCore { + /// Return a random value supporting the [`Standard`] distribution. + /// + /// [`Standard`]: distributions/struct.Standard.html + /// + /// # Example + /// + /// ``` + /// use rand::{thread_rng, Rng}; + /// + /// let mut rng = thread_rng(); + /// let x: u32 = rng.gen(); + /// println!("{}", x); + /// println!("{:?}", rng.gen::<(f64, bool)>()); + /// ``` + #[inline] + fn gen<T>(&mut self) -> T where Standard: Distribution<T> { + Standard.sample(self) + } + + /// Generate a random value in the range [`low`, `high`), i.e. inclusive of + /// `low` and exclusive of `high`. + /// + /// This is a convenience wrapper around + /// [`Uniform::sample_single`]. If this function will be called + /// repeatedly with the same arguments, it will likely be faster to + /// construct a [`Uniform`] distribution object and sample from that; this + /// allows amortization of the computations that allow for perfect + /// uniformity within the [`Uniform::new`] constructor. + /// + /// # Panics + /// + /// Panics if `low >= high`. + /// + /// # Example + /// + /// ``` + /// 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); + /// ``` + /// + /// [`Uniform`]: distributions/uniform/struct.Uniform.html + /// [`Uniform::new`]: distributions/uniform/struct.Uniform.html#method.new + /// [`Uniform::sample_single`]: distributions/uniform/struct.Uniform.html#method.sample_single + fn gen_range<T: PartialOrd + SampleUniform>(&mut self, low: T, high: T) -> T { + Uniform::sample_single(low, high, self) + } + + /// Sample a new value, using the given distribution. + /// + /// ### Example + /// + /// ``` + /// use rand::{thread_rng, Rng}; + /// use rand::distributions::Uniform; + /// + /// let mut rng = thread_rng(); + /// let x = rng.sample(Uniform::new(10u32, 15)); + /// // Type annotation requires two types, the type and distribution; the + /// // distribution can be inferred. + /// let y = rng.sample::<u16, _>(Uniform::new(10, 15)); + /// ``` + fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T { + distr.sample(self) + } + + /// Create an iterator that generates values using the given distribution. + /// + /// # Example + /// + /// ``` + /// use rand::{thread_rng, Rng}; + /// use rand::distributions::{Alphanumeric, Uniform, Standard}; + /// + /// let mut rng = thread_rng(); + /// + /// // Vec of 16 x f32: + /// let v: Vec<f32> = thread_rng().sample_iter(&Standard).take(16).collect(); + /// + /// // String: + /// let s: String = rng.sample_iter(&Alphanumeric).take(7).collect(); + /// + /// // Combined values + /// println!("{:?}", thread_rng().sample_iter(&Standard).take(5) + /// .collect::<Vec<(f64, bool)>>()); + /// + /// // Dice-rolling: + /// let die_range = Uniform::new_inclusive(1, 6); + /// let mut roll_die = rng.sample_iter(&die_range); + /// while roll_die.next().unwrap() != 6 { + /// println!("Not a 6; rolling again!"); + /// } + /// ``` + fn sample_iter<'a, T, D: Distribution<T>>(&'a mut self, distr: &'a D) + -> distributions::DistIter<'a, D, Self, T> where Self: Sized + { + distr.sample_iter(self) + } + + /// Fill `dest` entirely with random bytes (uniform value distribution), + /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices + /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). + /// + /// On big-endian platforms this performs byte-swapping to ensure + /// portability of results from reproducible generators. + /// + /// This uses [`fill_bytes`] internally which may handle some RNG errors + /// implicitly (e.g. waiting if the OS generator is not ready), but panics + /// on other errors. See also [`try_fill`] which returns errors. + /// + /// # Example + /// + /// ``` + /// use rand::{thread_rng, Rng}; + /// + /// let mut arr = [0i8; 20]; + /// thread_rng().fill(&mut arr[..]); + /// ``` + /// + /// [`fill_bytes`]: trait.RngCore.html#method.fill_bytes + /// [`try_fill`]: trait.Rng.html#method.try_fill + /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html + fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) { + self.fill_bytes(dest.as_byte_slice_mut()); + dest.to_le(); + } + + /// Fill `dest` entirely with random bytes (uniform value distribution), + /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices + /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.). + /// + /// On big-endian platforms this performs byte-swapping to ensure + /// portability of results from reproducible generators. + /// + /// This uses [`try_fill_bytes`] internally and forwards all RNG errors. In + /// some cases errors may be resolvable; see [`ErrorKind`] and + /// documentation for the RNG in use. If you do not plan to handle these + /// errors you may prefer to use [`fill`]. + /// + /// # Example + /// + /// ``` + /// # use rand::Error; + /// use rand::{thread_rng, Rng}; + /// + /// # fn try_inner() -> Result<(), Error> { + /// let mut arr = [0u64; 4]; + /// thread_rng().try_fill(&mut arr[..])?; + /// # Ok(()) + /// # } + /// + /// # try_inner().unwrap() + /// ``` + /// + /// [`ErrorKind`]: enum.ErrorKind.html + /// [`try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes + /// [`fill`]: trait.Rng.html#method.fill + /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html + fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> { + self.try_fill_bytes(dest.as_byte_slice_mut())?; + dest.to_le(); + Ok(()) + } + + /// Return a bool with a probability `p` of being true. + /// + /// This is a wrapper around [`distributions::Bernoulli`]. + /// + /// # Example + /// + /// ``` + /// use rand::{thread_rng, Rng}; + /// + /// let mut rng = thread_rng(); + /// println!("{}", rng.gen_bool(1.0 / 3.0)); + /// ``` + /// + /// # Panics + /// + /// If `p` < 0 or `p` > 1. + /// + /// [`distributions::Bernoulli`]: distributions/bernoulli/struct.Bernoulli.html + #[inline] + fn gen_bool(&mut self, p: f64) -> bool { + let d = distributions::Bernoulli::new(p); + self.sample(d) + } + + /// 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> { + 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> { + 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_algori...) + /// which produces an unbiased permutation. + /// + /// # Example + /// + /// ``` + /// 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]) { + 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)); + } + } + + /// Return an iterator that will yield an infinite number of randomly + /// generated items. + /// + /// # Example + /// + /// ``` + /// # #![allow(deprecated)] + /// 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)>>()); + /// ``` + #[allow(deprecated)] + #[deprecated(since="0.5.0", note="use Rng::sample_iter(&Standard) instead")] + fn gen_iter<T>(&mut self) -> Generator<T, &mut Self> where Standard: Distribution<T> { + Generator { rng: self, _marker: marker::PhantomData } + } + + /// Return a bool with a 1 in n chance of true + /// + /// # Example + /// + /// ``` + /// # #![allow(deprecated)] + /// use rand::{thread_rng, Rng}; + /// + /// let mut rng = thread_rng(); + /// assert_eq!(rng.gen_weighted_bool(0), true); + /// assert_eq!(rng.gen_weighted_bool(1), true); + /// // Just like `rng.gen::<bool>()` a 50-50% chance, but using a slower + /// // method with different results. + /// println!("{}", rng.gen_weighted_bool(2)); + /// // First meaningful use of `gen_weighted_bool`. + /// println!("{}", rng.gen_weighted_bool(3)); + /// ``` + #[deprecated(since="0.5.0", note="use gen_bool instead")] + fn gen_weighted_bool(&mut self, n: u32) -> bool { + // Short-circuit after `n <= 1` to avoid panic in `gen_range` + n <= 1 || self.gen_range(0, n) == 0 + } + + /// Return an iterator of random characters from the set A-Z,a-z,0-9. + /// + /// # Example + /// + /// ``` + /// # #![allow(deprecated)] + /// use rand::{thread_rng, Rng}; + /// + /// let s: String = thread_rng().gen_ascii_chars().take(10).collect(); + /// println!("{}", s); + /// ``` + #[allow(deprecated)] + #[deprecated(since="0.5.0", note="use sample_iter(&Alphanumeric) instead")] + fn gen_ascii_chars(&mut self) -> AsciiGenerator<&mut Self> { + AsciiGenerator { rng: self } + } +} + +impl<R: RngCore + ?Sized> Rng for R {} + +/// Trait for casting types to byte slices +/// +/// This is used by the [`fill`] and [`try_fill`] methods. +/// +/// [`fill`]: trait.Rng.html#method.fill +/// [`try_fill`]: trait.Rng.html#method.try_fill +pub trait AsByteSliceMut { + /// Return a mutable reference to self as a byte slice + fn as_byte_slice_mut(&mut self) -> &mut [u8]; + + /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms). + fn to_le(&mut self); +} + +impl AsByteSliceMut for [u8] { + fn as_byte_slice_mut(&mut self) -> &mut [u8] { + self + } + + fn to_le(&mut self) {} +} + +macro_rules! impl_as_byte_slice { + ($t:ty) => { + impl AsByteSliceMut for [$t] { + fn as_byte_slice_mut(&mut self) -> &mut [u8] { + unsafe { + slice::from_raw_parts_mut(&mut self[0] + as *mut $t + as *mut u8, + self.len() * mem::size_of::<$t>() + ) + } + } + + fn to_le(&mut self) { + for x in self { + *x = x.to_le(); + } + } + } + } +} + +impl_as_byte_slice!(u16); +impl_as_byte_slice!(u32); +impl_as_byte_slice!(u64); +#[cfg(feature="i128_support")] impl_as_byte_slice!(u128); +impl_as_byte_slice!(usize); +impl_as_byte_slice!(i8); +impl_as_byte_slice!(i16); +impl_as_byte_slice!(i32); +impl_as_byte_slice!(i64); +#[cfg(feature="i128_support")] impl_as_byte_slice!(i128); +impl_as_byte_slice!(isize); + +macro_rules! impl_as_byte_slice_arrays { + ($n:expr,) => {}; + ($n:expr, $N:ident, $($NN:ident,)*) => { + impl_as_byte_slice_arrays!($n - 1, $($NN,)*); + + impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut { + fn as_byte_slice_mut(&mut self) -> &mut [u8] { + self[..].as_byte_slice_mut() + } + + fn to_le(&mut self) { + self[..].to_le() + } + } + }; + (!div $n:expr,) => {}; + (!div $n:expr, $N:ident, $($NN:ident,)*) => { + impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*); + + impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut { + fn as_byte_slice_mut(&mut self) -> &mut [u8] { + self[..].as_byte_slice_mut() + } + + fn to_le(&mut self) { + self[..].to_le() + } + } + }; +} +impl_as_byte_slice_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,); +impl_as_byte_slice_arrays!(!div 4096, N,N,N,N,N,N,N,); + +/// 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)] +#[allow(deprecated)] +#[deprecated(since="0.5.0", note="use Rng::sample_iter instead")] +pub struct Generator<T, R: RngCore> { + rng: R, + _marker: marker::PhantomData<fn() -> T>, +} + +#[allow(deprecated)] +impl<T, R: RngCore> Iterator for Generator<T, R> where Standard: Distribution<T> { + 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)] +#[allow(deprecated)] +#[deprecated(since="0.5.0", note="use distributions::Alphanumeric instead")] +pub struct AsciiGenerator<R: RngCore> { + rng: R, +} + +#[allow(deprecated)] +impl<R: RngCore> Iterator for AsciiGenerator<R> { + type Item = char; + + fn next(&mut self) -> Option<char> { + const GEN_ASCII_STR_CHARSET: &[u8] = + b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\ + abcdefghijklmnopqrstuvwxyz\ + 0123456789"; + Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char) + } +} + + +/// A convenience extension to [`SeedableRng`] allowing construction from fresh +/// entropy. This trait is automatically implemented for any PRNG implementing +/// [`SeedableRng`] and is not intended to be implemented by users. +/// +/// This is equivalent to using `SeedableRng::from_rng(EntropyRng::new())` then +/// unwrapping the result. +/// +/// Since this is convenient and secure, it is the recommended way to create +/// PRNGs, though two alternatives may be considered: +/// +/// * Deterministic creation using [`SeedableRng::from_seed`] with a fixed seed +/// * Seeding from `thread_rng`: `SeedableRng::from_rng(thread_rng())?`; +/// this will usually be faster and should also be secure, but requires +/// trusting one extra component. +/// +/// ## Example +/// +/// ``` +/// use rand::{Rng, FromEntropy}; +/// use rand::rngs::StdRng; +/// +/// let mut rng = StdRng::from_entropy(); +/// println!("Random die roll: {}", rng.gen_range(1, 7)); +/// ``` +/// +/// [`EntropyRng`]: rngs/struct.EntropyRng.html +/// [`SeedableRng`]: trait.SeedableRng.html +/// [`SeedableRng::from_seed`]: trait.SeedableRng.html#tymethod.from_seed +#[cfg(feature="std")] +pub trait FromEntropy: SeedableRng { + /// Creates a new instance, automatically seeded with fresh entropy. + /// + /// Normally this will use `OsRng`, but if that fails `JitterRng` will be + /// used instead. Both should be suitable for cryptography. It is possible + /// that both entropy sources will fail though unlikely; failures would + /// almost certainly be platform limitations or build issues, i.e. most + /// applications targetting PC/mobile platforms should not need to worry + /// about this failing. + /// + /// # Panics + /// + /// If all entropy sources fail this will panic. If you need to handle + /// errors, use the following code, equivalent aside from error handling: + /// + /// ``` + /// # use rand::Error; + /// use rand::prelude::*; + /// use rand::rngs::EntropyRng; + /// + /// # fn try_inner() -> Result<(), Error> { + /// // This uses StdRng, but is valid for any R: SeedableRng + /// let mut rng = StdRng::from_rng(EntropyRng::new())?; + /// + /// println!("random number: {}", rng.gen_range(1, 10)); + /// # Ok(()) + /// # } + /// + /// # try_inner().unwrap() + /// ``` + fn from_entropy() -> Self; +} + +#[cfg(feature="std")] +impl<R: SeedableRng> FromEntropy for R { + fn from_entropy() -> R { + R::from_rng(EntropyRng::new()).unwrap_or_else(|err| + panic!("FromEntropy::from_entropy() failed: {}", err)) + } +} + + +/// DEPRECATED: use [`SmallRng`] instead. +/// +/// 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`. +/// +/// [`SmallRng`]: rngs/struct.SmallRng.html +#[deprecated(since="0.5.0", note="removed in favor of SmallRng")] +#[cfg(feature="std")] +pub fn weak_rng() -> XorShiftRng { + XorShiftRng::from_rng(thread_rng()).unwrap_or_else(|err| + panic!("weak_rng failed: {:?}", err)) +} + +/// Generates a random value using the thread-local random number generator. +/// +/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for +/// documentation of the entropy source and [`Standard`] for documentation of +/// distributions and type-specific generation. +/// +/// # 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!"); +/// } +/// ``` +/// +/// If you're calling `random()` in a loop, caching the generator as in the +/// following example can increase performance. +/// +/// ``` +/// # #![allow(deprecated)] +/// 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(); +/// } +/// ``` +/// +/// [`thread_rng`]: fn.thread_rng.html +/// [`Standard`]: distributions/struct.Standard.html +#[cfg(feature="std")] +#[inline] +pub fn random<T>() -> T where Standard: Distribution<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 +/// +/// ``` +/// # #![allow(deprecated)] +/// use rand::{thread_rng, sample}; +/// +/// let mut rng = thread_rng(); +/// let sample = sample(&mut rng, 1..100, 5); +/// println!("{:?}", sample); +/// ``` +#[cfg(feature="std")] +#[inline] +#[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 rngs::mock::StepRng; + use super::*; + #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::boxed::Box; + + pub struct TestRng<R> { inner: R } + + impl<R: RngCore> RngCore for TestRng<R> { + 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, dest: &mut [u8]) { + self.inner.fill_bytes(dest) + } + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.inner.try_fill_bytes(dest) + } + } + + pub fn rng(seed: u64) -> TestRng<StdRng> { + // TODO: use from_hashable + let mut state = seed; + let mut seed = <StdRng as SeedableRng>::Seed::default(); + for x in seed.iter_mut() { + // PCG algorithm + const MUL: u64 = 6364136223846793005; + const INC: u64 = 11634580027462260723; + let oldstate = state; + state = oldstate.wrapping_mul(MUL).wrapping_add(INC); + + let xorshifted = (((oldstate >> 18) ^ oldstate) >> 27) as u32; + let rot = (oldstate >> 59) as u32; + *x = xorshifted.rotate_right(rot) as u8; + } + TestRng { inner: StdRng::from_seed(seed) } + } + + #[test] + fn test_fill_bytes_default() { + let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0); + + // 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 buffer = [0u8; 87]; + let v = &mut buffer[0..n]; + r.fill_bytes(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_fill() { + let x = 9041086907909331047; // a random u64 + let mut rng = StepRng::new(x, 0); + + // Convert to byte sequence and back to u64; byte-swap twice if BE. + let mut array = [0u64; 2]; + rng.fill(&mut array[..]); + assert_eq!(array, [x, x]); + assert_eq!(rng.next_u64(), x); + + // Convert to bytes then u32 in LE order + let mut array = [0u32; 2]; + rng.fill(&mut array[..]); + assert_eq!(array, [x as u32, (x >> 32) as u32]); + assert_eq!(rng.next_u32(), x as u32); + } + + #[test] + fn test_gen_range() { + let mut r = rng(101); + 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 = rng(102); + r.gen_range(5, -2); + } + + #[test] + #[should_panic] + fn test_gen_range_panic_usize() { + let mut r = rng(103); + r.gen_range(5, 2); + } + + #[test] + #[allow(deprecated)] + fn test_gen_weighted_bool() { + let mut r = rng(104); + assert_eq!(r.gen_weighted_bool(0), true); + assert_eq!(r.gen_weighted_bool(1), true); + } + + #[test] + fn test_gen_bool() { + let mut r = rng(105); + for _ in 0..5 { + assert_eq!(r.gen_bool(0.0), false); + assert_eq!(r.gen_bool(1.0), true); + } + } + + #[test] + fn test_choose() { + let mut r = rng(107); + 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 = rng(108); + 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_rng_trait_object() { + use distributions::{Distribution, Standard}; + let mut rng = rng(109); + let mut r = &mut rng as &mut RngCore; + 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); + let _c: u8 = Standard.sample(&mut r); + } + + #[test] + #[cfg(feature="alloc")] + fn test_rng_boxed_trait() { + use distributions::{Distribution, Standard}; + let rng = rng(110); + let mut r = Box::new(rng) as Box<RngCore>; + 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); + let _c: u8 = Standard.sample(&mut r); + } + + #[test] + #[cfg(feature="std")] + 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(); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prelude.rs b/crates/rand-0.5.0-pre.2/src/prelude.rs new file mode 100644 index 0000000..358c237 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prelude.rs @@ -0,0 +1,28 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Convenience re-export of common members +//! +//! Like the standard library's prelude, this module simplifies importing of +//! common items. Unlike the standard prelude, the contents of this module must +//! be imported manually: +//! +//! ``` +//! use rand::prelude::*; +//! # let _ = StdRng::from_entropy(); +//! # let mut r = SmallRng::from_rng(thread_rng()).unwrap(); +//! # let _: f32 = r.gen(); +//! ``` + +#[doc(no_inline)] pub use distributions::Distribution; +#[doc(no_inline)] pub use rngs::{SmallRng, StdRng}; +#[doc(no_inline)] #[cfg(feature="std")] pub use rngs::ThreadRng; +#[doc(no_inline)] pub use {Rng, RngCore, CryptoRng, SeedableRng}; +#[doc(no_inline)] #[cfg(feature="std")] pub use {FromEntropy, random, thread_rng}; diff --git a/crates/rand-0.5.0-pre.2/src/prng/chacha.rs b/crates/rand-0.5.0-pre.2/src/prng/chacha.rs new file mode 100644 index 0000000..c81af62 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/chacha.rs @@ -0,0 +1,477 @@ +// Copyright 2014 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://www.rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The ChaCha random number generator. + +use core::fmt; +use rand_core::{CryptoRng, RngCore, SeedableRng, Error, le}; +use rand_core::block::{BlockRngCore, BlockRng}; + +const SEED_WORDS: usize = 8; // 8 words for the 256-bit key +const STATE_WORDS: usize = 16; + +/// A cryptographically secure random number generator that uses the ChaCha +/// algorithm. +/// +/// ChaCha is a stream cipher designed by Daniel J. Bernstein [1], that we use +/// as an RNG. It is an improved variant of the Salsa20 cipher family, which was +/// selected as one of the "stream ciphers suitable for widespread adoption" by +/// eSTREAM [2]. +/// +/// ChaCha uses add-rotate-xor (ARX) operations as its basis. These are safe +/// against timing attacks, although that is mostly a concern for ciphers and +/// not for RNGs. Also it is very suitable for SIMD implementation. +/// Here we do not provide a SIMD implementation yet, except for what is +/// provided by auto-vectorisation. +/// +/// With the ChaCha algorithm it is possible to choose the number of rounds the +/// core algorithm should run. The number of rounds is a tradeoff between +/// performance and security, where 8 rounds is the minimum potentially +/// secure configuration, and 20 rounds is widely used as a conservative choice. +/// We use 20 rounds in this implementation, but hope to allow type-level +/// configuration in the future. +/// +/// We use a 64-bit counter and 64-bit stream identifier as in Benstein's +/// implementation [1] except that we use a stream identifier in place of a +/// nonce. A 64-bit counter over 64-byte (16 word) blocks allows 1 ZiB of output +/// before cycling, and the stream identifier allows 2⁶⁴ unique +/// streams of output per seed. Both counter and stream are initialized to zero +/// but may be set via [`set_word_pos`] and [`set_stream`]. +/// +/// The word layout is: +/// +/// ```text +/// constant constant constant constant +/// seed seed seed seed +/// seed seed seed seed +/// counter counter nonce nonce +/// ``` +/// +/// This implementation uses an output buffer of sixteen `u32` words, and uses +/// [`BlockRng`] to implement the [`RngCore`] methods. +/// +/// [1]: D. J. Bernstein, [*ChaCha, a variant of Salsa20*]( +/// https://cr.yp.to/chacha.html) +/// +/// [2]: [eSTREAM: the ECRYPT Stream Cipher Project]( +/// http://www.ecrypt.eu.org/stream/) +/// +/// [`set_word_pos`]: #method.set_word_pos +/// [`set_stream`]: #method.set_stream +/// [`BlockRng`]: ../../../rand_core/block/struct.BlockRng.html +/// [`RngCore`]: ../../trait.RngCore.html +#[derive(Clone, Debug)] +pub struct ChaChaRng(BlockRng<ChaChaCore>); + +impl RngCore for ChaChaRng { + #[inline] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + #[inline] + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest) + } + + #[inline] + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for ChaChaRng { + type Seed = <ChaChaCore as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + ChaChaRng(BlockRng::<ChaChaCore>::from_seed(seed)) + } + + fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { + BlockRng::<ChaChaCore>::from_rng(rng).map(ChaChaRng) + } +} + +impl CryptoRng for ChaChaRng {} + +impl ChaChaRng { + /// Create an ChaCha random number generator using the default + /// fixed key of 8 zero words. + /// + /// # Examples + /// + /// ``` + /// # #![allow(deprecated)] + /// use rand::{RngCore, 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 + #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")] + pub fn new_unseeded() -> ChaChaRng { + ChaChaRng::from_seed([0; SEED_WORDS*4]) + } + + /// Get the offset from the start of the stream, in 32-bit words. + /// + /// Since the generated blocks are 16 words (2⁴) long and the + /// counter is 64-bits, the offset is a 68-bit number. Sub-word offsets are + /// not supported, hence the result can simply be multiplied by 4 to get a + /// byte-offset. + /// + /// Note: this function is currently only available when the `i128_support` + /// feature is enabled. In the future this will be enabled by default. + #[cfg(feature = "i128_support")] + pub fn get_word_pos(&self) -> u128 { + let mut c = (self.0.core.state[13] as u64) << 32 + | (self.0.core.state[12] as u64); + let mut index = self.0.index(); + // c is the end of the last block generated, unless index is at end + if index >= STATE_WORDS { + index = 0; + } else { + c = c.wrapping_sub(1); + } + ((c as u128) << 4) | (index as u128) + } + + /// Set the offset from the start of the stream, in 32-bit words. + /// + /// As with `get_word_pos`, we use a 68-bit number. Since the generator + /// simply cycles at the end of its period (1 ZiB), we ignore the upper + /// 60 bits. + /// + /// Note: this function is currently only available when the `i128_support` + /// feature is enabled. In the future this will be enabled by default. + #[cfg(feature = "i128_support")] + pub fn set_word_pos(&mut self, word_offset: u128) { + let index = (word_offset as usize) & 0xF; + let counter = (word_offset >> 4) as u64; + self.0.core.state[12] = counter as u32; + self.0.core.state[13] = (counter >> 32) as u32; + if index != 0 { + self.0.generate_and_set(index); // also increments counter + } else { + self.0.reset(); + } + } + + /// Set the stream number. + /// + /// This is initialized to zero; 2⁶⁴ unique streams of output + /// are available per seed/key. + /// + /// Note that in order to reproduce ChaCha output with a specific 64-bit + /// nonce, one can convert that nonce to a `u64` in little-endian fashion + /// and pass to this function. In theory a 96-bit nonce can be used by + /// passing the last 64-bits to this function and using the first 32-bits as + /// the most significant half of the 64-bit counter (which may be set + /// indirectly via `set_word_pos`), but this is not directly supported. + pub fn set_stream(&mut self, stream: u64) { + let index = self.0.index(); + self.0.core.state[14] = stream as u32; + self.0.core.state[15] = (stream >> 32) as u32; + if index < STATE_WORDS { + // we need to regenerate a partial result buffer + { + // reverse of counter adjustment in generate() + if self.0.core.state[12] == 0 { + self.0.core.state[13] = self.0.core.state[13].wrapping_sub(1); + } + self.0.core.state[12] = self.0.core.state[12].wrapping_sub(1); + } + self.0.generate_and_set(index); + } + } +} + +/// The core of `ChaChaRng`, used with `BlockRng`. +#[derive(Clone)] +pub struct ChaChaCore { + state: [u32; STATE_WORDS], +} + +// Custom Debug implementation that does not expose the internal state +impl fmt::Debug for ChaChaCore { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!(f, "ChaChaCore {{}}") + } +} + +macro_rules! quarter_round{ + ($a: expr, $b: expr, $c: expr, $d: expr) => {{ + $a = $a.wrapping_add($b); $d ^= $a; $d = $d.rotate_left(16); + $c = $c.wrapping_add($d); $b ^= $c; $b = $b.rotate_left(12); + $a = $a.wrapping_add($b); $d ^= $a; $d = $d.rotate_left( 8); + $c = $c.wrapping_add($d); $b ^= $c; $b = $b.rotate_left( 7); + }} +} + +macro_rules! double_round{ + ($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]); + }} +} + +impl BlockRngCore for ChaChaCore { + type Item = u32; + type Results = [u32; STATE_WORDS]; + + fn generate(&mut self, results: &mut Self::Results) { + // For some reason extracting this part into a separate function + // improves performance by 50%. + fn core(results: &mut [u32; STATE_WORDS], + state: &[u32; STATE_WORDS]) + { + let mut tmp = *state; + let rounds = 20; + for _ in 0..rounds / 2 { + double_round!(tmp); + } + for i in 0..STATE_WORDS { + results[i] = tmp[i].wrapping_add(state[i]); + } + } + + core(results, &self.state); + + // update 64-bit counter + self.state[12] = self.state[12].wrapping_add(1); + if self.state[12] != 0 { return; }; + self.state[13] = self.state[13].wrapping_add(1); + } +} + +impl SeedableRng for ChaChaCore { + type Seed = [u8; SEED_WORDS*4]; + + fn from_seed(seed: Self::Seed) -> Self { + let mut seed_le = [0u32; SEED_WORDS]; + le::read_u32_into(&seed, &mut seed_le); + Self { + state: [0x61707865, 0x3320646E, 0x79622D32, 0x6B206574, // constants + seed_le[0], seed_le[1], seed_le[2], seed_le[3], // seed + seed_le[4], seed_le[5], seed_le[6], seed_le[7], // seed + 0, 0, 0, 0], // counter + } + } +} + +impl CryptoRng for ChaChaCore {} + +impl From<ChaChaCore> for ChaChaRng { + fn from(core: ChaChaCore) -> Self { + ChaChaRng(BlockRng::new(core)) + } +} + +#[cfg(test)] +mod test { + use {RngCore, SeedableRng}; + use super::ChaChaRng; + + #[test] + fn test_chacha_construction() { + let seed = [0,0,0,0,0,0,0,0, + 1,0,0,0,0,0,0,0, + 2,0,0,0,0,0,0,0, + 3,0,0,0,0,0,0,0]; + let mut rng1 = ChaChaRng::from_seed(seed); + assert_eq!(rng1.next_u32(), 137206642); + + let mut rng2 = ChaChaRng::from_rng(rng1).unwrap(); + assert_eq!(rng2.next_u32(), 1325750369); + } + + #[test] + fn test_chacha_true_values_a() { + // Test vectors 1 and 2 from + // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04 + let seed = [0u8; 32]; + let mut rng = ChaChaRng::from_seed(seed); + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0xade0b876, 0x903df1a0, 0xe56a5d40, 0x28bd8653, + 0xb819d2bd, 0x1aed8da0, 0xccef36a8, 0xc70d778b, + 0x7c5941da, 0x8d485751, 0x3fe02477, 0x374ad8b8, + 0xf4b8436a, 0x1ca11815, 0x69b687c3, 0x8665eeb2]; + assert_eq!(results, expected); + + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0xbee7079f, 0x7a385155, 0x7c97ba98, 0x0d082d73, + 0xa0290fcb, 0x6965e348, 0x3e53c612, 0xed7aee32, + 0x7621b729, 0x434ee69c, 0xb03371d5, 0xd539d874, + 0x281fed31, 0x45fb0a51, 0x1f0ae1ac, 0x6f4d794b]; + assert_eq!(results, expected); + } + + #[test] + fn test_chacha_true_values_b() { + // Test vector 3 from + // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04 + let seed = [0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 1]; + let mut rng = ChaChaRng::from_seed(seed); + + // Skip block 0 + for _ in 0..16 { rng.next_u32(); } + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0x2452eb3a, 0x9249f8ec, 0x8d829d9b, 0xddd4ceb1, + 0xe8252083, 0x60818b01, 0xf38422b8, 0x5aaa49c9, + 0xbb00ca8e, 0xda3ba7b4, 0xc4b592d1, 0xfdf2732f, + 0x4436274e, 0x2561b3c8, 0xebdd4aa6, 0xa0136c00]; + assert_eq!(results, expected); + } + + #[test] + #[cfg(feature = "i128_support")] + fn test_chacha_true_values_c() { + // Test vector 4 from + // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04 + let seed = [0, 0xff, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0]; + let expected = [0xfb4dd572, 0x4bc42ef1, 0xdf922636, 0x327f1394, + 0xa78dea8f, 0x5e269039, 0xa1bebbc1, 0xcaf09aae, + 0xa25ab213, 0x48a6b46c, 0x1b9d9bcb, 0x092c5be6, + 0x546ca624, 0x1bec45d5, 0x87f47473, 0x96f0992e]; + let expected_end = 3 * 16; + let mut results = [0u32; 16]; + + // Test block 2 by skipping block 0 and 1 + let mut rng1 = ChaChaRng::from_seed(seed); + for _ in 0..32 { rng1.next_u32(); } + for i in results.iter_mut() { *i = rng1.next_u32(); } + assert_eq!(results, expected); + assert_eq!(rng1.get_word_pos(), expected_end); + + // Test block 2 by using `set_word_pos` + let mut rng2 = ChaChaRng::from_seed(seed); + rng2.set_word_pos(2 * 16); + for i in results.iter_mut() { *i = rng2.next_u32(); } + assert_eq!(results, expected); + assert_eq!(rng2.get_word_pos(), expected_end); + + // Test skipping behaviour with other types + let mut buf = [0u8; 32]; + rng2.fill_bytes(&mut buf[..]); + assert_eq!(rng2.get_word_pos(), expected_end + 8); + rng2.fill_bytes(&mut buf[0..25]); + assert_eq!(rng2.get_word_pos(), expected_end + 15); + rng2.next_u64(); + assert_eq!(rng2.get_word_pos(), expected_end + 17); + rng2.next_u32(); + rng2.next_u64(); + assert_eq!(rng2.get_word_pos(), expected_end + 20); + rng2.fill_bytes(&mut buf[0..1]); + assert_eq!(rng2.get_word_pos(), expected_end + 21); + } + + #[test] + fn test_chacha_multiple_blocks() { + let seed = [0,0,0,0, 1,0,0,0, 2,0,0,0, 3,0,0,0, 4,0,0,0, 5,0,0,0, 6,0,0,0, 7,0,0,0]; + let mut rng = ChaChaRng::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 results = [0u32; 16]; + for i in results.iter_mut() { + *i = rng.next_u32(); + for _ in 0..16 { + rng.next_u32(); + } + } + let expected = [0xf225c81a, 0x6ab1be57, 0x04d42951, 0x70858036, + 0x49884684, 0x64efec72, 0x4be2d186, 0x3615b384, + 0x11cfa18e, 0xd3c50049, 0x75c775f6, 0x434c6530, + 0x2c5bad8f, 0x898881dc, 0x5f1c86d9, 0xc1f8e7f4]; + assert_eq!(results, expected); + } + + #[test] + fn test_chacha_true_bytes() { + let seed = [0u8; 32]; + let mut rng = ChaChaRng::from_seed(seed); + let mut results = [0u8; 32]; + rng.fill_bytes(&mut results); + let expected = [118, 184, 224, 173, 160, 241, 61, 144, + 64, 93, 106, 229, 83, 134, 189, 40, + 189, 210, 25, 184, 160, 141, 237, 26, + 168, 54, 239, 204, 139, 119, 13, 199]; + assert_eq!(results, expected); + } + + #[test] + fn test_chacha_nonce() { + // Test vector 5 from + // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04 + // Although we do not support setting a nonce, we try it here anyway so + // we can use this test vector. + let seed = [0u8; 32]; + let mut rng = ChaChaRng::from_seed(seed); + // 96-bit nonce in LE order is: 0,0,0,0, 0,0,0,0, 0,0,0,2 + rng.set_stream(2u64 << (24 + 32)); + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0x374dc6c2, 0x3736d58c, 0xb904e24a, 0xcd3f93ef, + 0x88228b1a, 0x96a4dfb3, 0x5b76ab72, 0xc727ee54, + 0x0e0e978a, 0xf3145c95, 0x1b748ea8, 0xf786c297, + 0x99c28f5f, 0x628314e8, 0x398a19fa, 0x6ded1b53]; + assert_eq!(results, expected); + } + + #[test] + fn test_chacha_clone_streams() { + let seed = [0,0,0,0, 1,0,0,0, 2,0,0,0, 3,0,0,0, 4,0,0,0, 5,0,0,0, 6,0,0,0, 7,0,0,0]; + let mut rng = ChaChaRng::from_seed(seed); + let mut clone = rng.clone(); + for _ in 0..16 { + assert_eq!(rng.next_u64(), clone.next_u64()); + } + + rng.set_stream(51); + for _ in 0..7 { + assert!(rng.next_u32() != clone.next_u32()); + } + clone.set_stream(51); // switch part way through block + for _ in 7..16 { + assert_eq!(rng.next_u32(), clone.next_u32()); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prng/hc128.rs b/crates/rand-0.5.0-pre.2/src/prng/hc128.rs new file mode 100644 index 0000000..733975c --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/hc128.rs @@ -0,0 +1,463 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://www.rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The HC-128 random number generator. + +use core::fmt; +use rand_core::{CryptoRng, RngCore, SeedableRng, Error, le}; +use rand_core::block::{BlockRngCore, BlockRng}; + +const SEED_WORDS: usize = 8; // 128 bit key followed by 128 bit iv + +/// A cryptographically secure random number generator that uses the HC-128 +/// algorithm. +/// +/// HC-128 is a stream cipher designed by Hongjun Wu [1], that we use as an RNG. +/// It is selected as one of the "stream ciphers suitable for widespread +/// adoption" by eSTREAM [2]. +/// +/// HC-128 is an array based RNG. In this it is similar to RC-4 and ISAAC before +/// it, but those have never been proven cryptographically secure (or have even +/// been significantly compromised, as in the case of RC-4 [5]). +/// +/// Because HC-128 works with simple indexing into a large array and with a few +/// operations that parallelize well, it has very good performance. The size of +/// the array it needs, 4kb, can however be a disadvantage. +/// +/// This implementation is not based on the version of HC-128 submitted to the +/// eSTREAM contest, but on a later version by the author with a few small +/// improvements from December 15, 2009 [3]. +/// +/// HC-128 has no known weaknesses that are easier to exploit than doing a +/// brute-force search of 2¹²⁸. A very comprehensive analysis of the +/// current state of known attacks / weaknesses of HC-128 is given in [4]. +/// +/// The average cycle length is expected to be +/// 2^1024*32+10-1 = 2³²⁷⁷⁷. +/// We support seeding with a 256-bit array, which matches the 128-bit key +/// concatenated with a 128-bit IV from the stream cipher. +/// +/// This implementation uses an output buffer of sixteen `u32` words, and uses +/// [`BlockRng`] to implement the [`RngCore`] methods. +/// +/// ## References +/// [1]: Hongjun Wu (2008). ["The Stream Cipher HC-128"]( +/// http://www.ecrypt.eu.org/stream/p3ciphers/hc/hc128_p3.pdf). +/// *The eSTREAM Finalists*, LNCS 4986, pp. 39–47, Springer-Verlag. +/// +/// [2]: [eSTREAM: the ECRYPT Stream Cipher Project]( +/// http://www.ecrypt.eu.org/stream/) +/// +/// [3]: Hongjun Wu, [Stream Ciphers HC-128 and HC-256]( +/// https://www.ntu.edu.sg/home/wuhj/research/hc/index.html) +/// +/// [4]: Shashwat Raizada (January 2015),["Some Results On Analysis And +/// Implementation Of HC-128 Stream Cipher"]( +/// http://library.isical.ac.in:8080/jspui/bitstream/123456789/6636/1/TH431.pdf). +/// +/// [5]: Internet Engineering Task Force (Februari 2015), +/// ["Prohibiting RC4 Cipher Suites"](https://tools.ietf.org/html/rfc7465). +/// +/// [`BlockRng`]: ../../../rand_core/block/struct.BlockRng.html +/// [`RngCore`]: ../../trait.RngCore.html +#[derive(Clone, Debug)] +pub struct Hc128Rng(BlockRng<Hc128Core>); + +impl RngCore for Hc128Rng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for Hc128Rng { + type Seed = <Hc128Core as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + Hc128Rng(BlockRng::<Hc128Core>::from_seed(seed)) + } + + fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { + BlockRng::<Hc128Core>::from_rng(rng).map(Hc128Rng) + } +} + +impl CryptoRng for Hc128Rng {} + +/// The core of `Hc128Rng`, used with `BlockRng`. +#[derive(Clone)] +pub struct Hc128Core { + t: [u32; 1024], + counter1024: usize, +} + +// Custom Debug implementation that does not expose the internal state +impl fmt::Debug for Hc128Core { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!(f, "Hc128Core {{}}") + } +} + +impl BlockRngCore for Hc128Core { + type Item = u32; + type Results = [u32; 16]; + + fn generate(&mut self, results: &mut Self::Results) { + assert!(self.counter1024 % 16 == 0); + + let cc = self.counter1024 % 512; + let dd = (cc + 16) % 512; + let ee = cc.wrapping_sub(16) % 512; + + if self.counter1024 & 512 == 0 { + // P block + results[0] = self.step_p(cc+0, cc+1, ee+13, ee+6, ee+4); + results[1] = self.step_p(cc+1, cc+2, ee+14, ee+7, ee+5); + results[2] = self.step_p(cc+2, cc+3, ee+15, ee+8, ee+6); + results[3] = self.step_p(cc+3, cc+4, cc+0, ee+9, ee+7); + results[4] = self.step_p(cc+4, cc+5, cc+1, ee+10, ee+8); + results[5] = self.step_p(cc+5, cc+6, cc+2, ee+11, ee+9); + results[6] = self.step_p(cc+6, cc+7, cc+3, ee+12, ee+10); + results[7] = self.step_p(cc+7, cc+8, cc+4, ee+13, ee+11); + results[8] = self.step_p(cc+8, cc+9, cc+5, ee+14, ee+12); + results[9] = self.step_p(cc+9, cc+10, cc+6, ee+15, ee+13); + results[10] = self.step_p(cc+10, cc+11, cc+7, cc+0, ee+14); + results[11] = self.step_p(cc+11, cc+12, cc+8, cc+1, ee+15); + results[12] = self.step_p(cc+12, cc+13, cc+9, cc+2, cc+0); + results[13] = self.step_p(cc+13, cc+14, cc+10, cc+3, cc+1); + results[14] = self.step_p(cc+14, cc+15, cc+11, cc+4, cc+2); + results[15] = self.step_p(cc+15, dd+0, cc+12, cc+5, cc+3); + } else { + // Q block + results[0] = self.step_q(cc+0, cc+1, ee+13, ee+6, ee+4); + results[1] = self.step_q(cc+1, cc+2, ee+14, ee+7, ee+5); + results[2] = self.step_q(cc+2, cc+3, ee+15, ee+8, ee+6); + results[3] = self.step_q(cc+3, cc+4, cc+0, ee+9, ee+7); + results[4] = self.step_q(cc+4, cc+5, cc+1, ee+10, ee+8); + results[5] = self.step_q(cc+5, cc+6, cc+2, ee+11, ee+9); + results[6] = self.step_q(cc+6, cc+7, cc+3, ee+12, ee+10); + results[7] = self.step_q(cc+7, cc+8, cc+4, ee+13, ee+11); + results[8] = self.step_q(cc+8, cc+9, cc+5, ee+14, ee+12); + results[9] = self.step_q(cc+9, cc+10, cc+6, ee+15, ee+13); + results[10] = self.step_q(cc+10, cc+11, cc+7, cc+0, ee+14); + results[11] = self.step_q(cc+11, cc+12, cc+8, cc+1, ee+15); + results[12] = self.step_q(cc+12, cc+13, cc+9, cc+2, cc+0); + results[13] = self.step_q(cc+13, cc+14, cc+10, cc+3, cc+1); + results[14] = self.step_q(cc+14, cc+15, cc+11, cc+4, cc+2); + results[15] = self.step_q(cc+15, dd+0, cc+12, cc+5, cc+3); + } + self.counter1024 = self.counter1024.wrapping_add(16); + } +} + +impl Hc128Core { + // One step of HC-128, update P and generate 32 bits keystream + #[inline(always)] + fn step_p(&mut self, i: usize, i511: usize, i3: usize, i10: usize, i12: usize) + -> u32 + { + let (p, q) = self.t.split_at_mut(512); + // FIXME: it would be great if we the bounds checks here could be + // optimized out, and we would not need unsafe. + // This improves performance by about 7%. + unsafe { + let temp0 = p.get_unchecked(i511).rotate_right(23); + let temp1 = p.get_unchecked(i3).rotate_right(10); + let temp2 = p.get_unchecked(i10).rotate_right(8); + *p.get_unchecked_mut(i) = p.get_unchecked(i) + .wrapping_add(temp2) + .wrapping_add(temp0 ^ temp1); + let temp3 = { + // The h1 function in HC-128 + let a = *p.get_unchecked(i12) as u8; + let c = (p.get_unchecked(i12) >> 16) as u8; + q[a as usize].wrapping_add(q[256 + c as usize]) + }; + temp3 ^ p.get_unchecked(i) + } + } + + // One step of HC-128, update Q and generate 32 bits keystream + // Similar to `step_p`, but `p` and `q` are swapped, and the rotates are to + // the left instead of to the right. + #[inline(always)] + fn step_q(&mut self, i: usize, i511: usize, i3: usize, i10: usize, i12: usize) + -> u32 + { + let (p, q) = self.t.split_at_mut(512); + unsafe { + let temp0 = q.get_unchecked(i511).rotate_left(23); + let temp1 = q.get_unchecked(i3).rotate_left(10); + let temp2 = q.get_unchecked(i10).rotate_left(8); + *q.get_unchecked_mut(i) = q.get_unchecked(i) + .wrapping_add(temp2) + .wrapping_add(temp0 ^ temp1); + let temp3 = { + // The h2 function in HC-128 + let a = *q.get_unchecked(i12) as u8; + let c = (q.get_unchecked(i12) >> 16) as u8; + p[a as usize].wrapping_add(p[256 + c as usize]) + }; + temp3 ^ q.get_unchecked(i) + } + } + + fn sixteen_steps(&mut self) { + assert!(self.counter1024 % 16 == 0); + + let cc = self.counter1024 % 512; + let dd = (cc + 16) % 512; + let ee = cc.wrapping_sub(16) % 512; + + if self.counter1024 < 512 { + // P block + self.t[cc+0] = self.step_p(cc+0, cc+1, ee+13, ee+6, ee+4); + self.t[cc+1] = self.step_p(cc+1, cc+2, ee+14, ee+7, ee+5); + self.t[cc+2] = self.step_p(cc+2, cc+3, ee+15, ee+8, ee+6); + self.t[cc+3] = self.step_p(cc+3, cc+4, cc+0, ee+9, ee+7); + self.t[cc+4] = self.step_p(cc+4, cc+5, cc+1, ee+10, ee+8); + self.t[cc+5] = self.step_p(cc+5, cc+6, cc+2, ee+11, ee+9); + self.t[cc+6] = self.step_p(cc+6, cc+7, cc+3, ee+12, ee+10); + self.t[cc+7] = self.step_p(cc+7, cc+8, cc+4, ee+13, ee+11); + self.t[cc+8] = self.step_p(cc+8, cc+9, cc+5, ee+14, ee+12); + self.t[cc+9] = self.step_p(cc+9, cc+10, cc+6, ee+15, ee+13); + self.t[cc+10] = self.step_p(cc+10, cc+11, cc+7, cc+0, ee+14); + self.t[cc+11] = self.step_p(cc+11, cc+12, cc+8, cc+1, ee+15); + self.t[cc+12] = self.step_p(cc+12, cc+13, cc+9, cc+2, cc+0); + self.t[cc+13] = self.step_p(cc+13, cc+14, cc+10, cc+3, cc+1); + self.t[cc+14] = self.step_p(cc+14, cc+15, cc+11, cc+4, cc+2); + self.t[cc+15] = self.step_p(cc+15, dd+0, cc+12, cc+5, cc+3); + } else { + // Q block + self.t[cc+512+0] = self.step_q(cc+0, cc+1, ee+13, ee+6, ee+4); + self.t[cc+512+1] = self.step_q(cc+1, cc+2, ee+14, ee+7, ee+5); + self.t[cc+512+2] = self.step_q(cc+2, cc+3, ee+15, ee+8, ee+6); + self.t[cc+512+3] = self.step_q(cc+3, cc+4, cc+0, ee+9, ee+7); + self.t[cc+512+4] = self.step_q(cc+4, cc+5, cc+1, ee+10, ee+8); + self.t[cc+512+5] = self.step_q(cc+5, cc+6, cc+2, ee+11, ee+9); + self.t[cc+512+6] = self.step_q(cc+6, cc+7, cc+3, ee+12, ee+10); + self.t[cc+512+7] = self.step_q(cc+7, cc+8, cc+4, ee+13, ee+11); + self.t[cc+512+8] = self.step_q(cc+8, cc+9, cc+5, ee+14, ee+12); + self.t[cc+512+9] = self.step_q(cc+9, cc+10, cc+6, ee+15, ee+13); + self.t[cc+512+10] = self.step_q(cc+10, cc+11, cc+7, cc+0, ee+14); + self.t[cc+512+11] = self.step_q(cc+11, cc+12, cc+8, cc+1, ee+15); + self.t[cc+512+12] = self.step_q(cc+12, cc+13, cc+9, cc+2, cc+0); + self.t[cc+512+13] = self.step_q(cc+13, cc+14, cc+10, cc+3, cc+1); + self.t[cc+512+14] = self.step_q(cc+14, cc+15, cc+11, cc+4, cc+2); + self.t[cc+512+15] = self.step_q(cc+15, dd+0, cc+12, cc+5, cc+3); + } + self.counter1024 += 16; + } + + // Initialize an HC-128 random number generator. The seed has to be + // 256 bits in length (`[u32; 8]`), matching the 128 bit `key` followed by + // 128 bit `iv` when HC-128 where to be used as a stream cipher. + fn init(seed: [u32; SEED_WORDS]) -> Self { + #[inline] + fn f1(x: u32) -> u32 { + x.rotate_right(7) ^ x.rotate_right(18) ^ (x >> 3) + } + + #[inline] + fn f2(x: u32) -> u32 { + x.rotate_right(17) ^ x.rotate_right(19) ^ (x >> 10) + } + + let mut t = [0u32; 1024]; + + // Expand the key and iv into P and Q + let (key, iv) = seed.split_at(4); + t[..4].copy_from_slice(key); + t[4..8].copy_from_slice(key); + t[8..12].copy_from_slice(iv); + t[12..16].copy_from_slice(iv); + + // Generate the 256 intermediate values W[16] ... W[256+16-1], and + // copy the last 16 generated values to the start op P. + for i in 16..256+16 { + t[i] = f2(t[i-2]).wrapping_add(t[i-7]).wrapping_add(f1(t[i-15])) + .wrapping_add(t[i-16]).wrapping_add(i as u32); + } + { + let (p1, p2) = t.split_at_mut(256); + p1[0..16].copy_from_slice(&p2[0..16]); + } + + // Generate both the P and Q tables + for i in 16..1024 { + t[i] = f2(t[i-2]).wrapping_add(t[i-7]).wrapping_add(f1(t[i-15])) + .wrapping_add(t[i-16]).wrapping_add(256 + i as u32); + } + + let mut core = Self { t, counter1024: 0 }; + + // run the cipher 1024 steps + for _ in 0..64 { core.sixteen_steps() }; + core.counter1024 = 0; + core + } +} + +impl SeedableRng for Hc128Core { + type Seed = [u8; SEED_WORDS*4]; + + /// Create an HC-128 random number generator with a seed. The seed has to be + /// 256 bits in length, matching the 128 bit `key` followed by 128 bit `iv` + /// when HC-128 where to be used as a stream cipher. + fn from_seed(seed: Self::Seed) -> Self { + let mut seed_u32 = [0u32; SEED_WORDS]; + le::read_u32_into(&seed, &mut seed_u32); + Self::init(seed_u32) + } +} + +impl CryptoRng for Hc128Core {} + +#[cfg(test)] +mod test { + use {RngCore, SeedableRng}; + use super::Hc128Rng; + + #[test] + // Test vector 1 from the paper "The Stream Cipher HC-128" + fn test_hc128_true_values_a() { + let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng = Hc128Rng::from_seed(seed); + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0x73150082, 0x3bfd03a0, 0xfb2fd77f, 0xaa63af0e, + 0xde122fc6, 0xa7dc29b6, 0x62a68527, 0x8b75ec68, + 0x9036db1e, 0x81896005, 0x00ade078, 0x491fbf9a, + 0x1cdc3013, 0x6c3d6e24, 0x90f664b2, 0x9cd57102]; + assert_eq!(results, expected); + } + + #[test] + // Test vector 2 from the paper "The Stream Cipher HC-128" + fn test_hc128_true_values_b() { + let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 1,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng = Hc128Rng::from_seed(seed); + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0xc01893d5, 0xb7dbe958, 0x8f65ec98, 0x64176604, + 0x36fc6724, 0xc82c6eec, 0x1b1c38a7, 0xc9b42a95, + 0x323ef123, 0x0a6a908b, 0xce757b68, 0x9f14f7bb, + 0xe4cde011, 0xaeb5173f, 0x89608c94, 0xb5cf46ca]; + assert_eq!(results, expected); + } + + #[test] + // Test vector 3 from the paper "The Stream Cipher HC-128" + fn test_hc128_true_values_c() { + let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng = Hc128Rng::from_seed(seed); + + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected = [0x518251a4, 0x04b4930a, 0xb02af931, 0x0639f032, + 0xbcb4a47a, 0x5722480b, 0x2bf99f72, 0xcdc0e566, + 0x310f0c56, 0xd3cc83e8, 0x663db8ef, 0x62dfe07f, + 0x593e1790, 0xc5ceaa9c, 0xab03806f, 0xc9a6e5a0]; + assert_eq!(results, expected); + } + + #[test] + fn test_hc128_true_values_u64() { + let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng = Hc128Rng::from_seed(seed); + + let mut results = [0u64; 8]; + for i in results.iter_mut() { *i = rng.next_u64(); } + let expected = [0x3bfd03a073150082, 0xaa63af0efb2fd77f, + 0xa7dc29b6de122fc6, 0x8b75ec6862a68527, + 0x818960059036db1e, 0x491fbf9a00ade078, + 0x6c3d6e241cdc3013, 0x9cd5710290f664b2]; + assert_eq!(results, expected); + + // The RNG operates in a P block of 512 results and next a Q block. + // After skipping 2*800 u32 results we end up somewhere in the Q block + // of the second round + for _ in 0..800 { rng.next_u64(); } + + for i in results.iter_mut() { *i = rng.next_u64(); } + let expected = [0xd8c4d6ca84d0fc10, 0xf16a5d91dc66e8e7, + 0xd800de5bc37a8653, 0x7bae1f88c0dfbb4c, + 0x3bfe1f374e6d4d14, 0x424b55676be3fa06, + 0xe3a1e8758cbff579, 0x417f7198c5652bcd]; + assert_eq!(results, expected); + } + + #[test] + fn test_hc128_true_values_bytes() { + let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng = Hc128Rng::from_seed(seed); + let expected = [0x31, 0xf9, 0x2a, 0xb0, 0x32, 0xf0, 0x39, 0x06, + 0x7a, 0xa4, 0xb4, 0xbc, 0x0b, 0x48, 0x22, 0x57, + 0x72, 0x9f, 0xf9, 0x2b, 0x66, 0xe5, 0xc0, 0xcd, + 0x56, 0x0c, 0x0f, 0x31, 0xe8, 0x83, 0xcc, 0xd3, + 0xef, 0xb8, 0x3d, 0x66, 0x7f, 0xe0, 0xdf, 0x62, + 0x90, 0x17, 0x3e, 0x59, 0x9c, 0xaa, 0xce, 0xc5, + 0x6f, 0x80, 0x03, 0xab, 0xa0, 0xe5, 0xa6, 0xc9, + 0x60, 0x95, 0x84, 0x7a, 0xa5, 0x68, 0x5a, 0x84, + 0xea, 0xd5, 0xf3, 0xea, 0x73, 0xa9, 0xad, 0x01, + 0x79, 0x7d, 0xbe, 0x9f, 0xea, 0xe3, 0xf9, 0x74, + 0x0e, 0xda, 0x2f, 0xa0, 0xe4, 0x7b, 0x4b, 0x1b, + 0xdd, 0x17, 0x69, 0x4a, 0xfe, 0x9f, 0x56, 0x95, + 0xad, 0x83, 0x6b, 0x9d, 0x60, 0xa1, 0x99, 0x96, + 0x90, 0x00, 0x66, 0x7f, 0xfa, 0x7e, 0x65, 0xe9, + 0xac, 0x8b, 0x92, 0x34, 0x77, 0xb4, 0x23, 0xd0, + 0xb9, 0xab, 0xb1, 0x47, 0x7d, 0x4a, 0x13, 0x0a]; + + // Pick a somewhat large buffer so we can test filling with the + // remainder from `state.results`, directly filling the buffer, and + // filling the remainder of the buffer. + let mut buffer = [0u8; 16*4*2]; + // Consume a value so that we have a remainder. + assert!(rng.next_u64() == 0x04b4930a518251a4); + rng.fill_bytes(&mut buffer); + + // [u8; 128] doesn't implement PartialEq + assert_eq!(buffer.len(), expected.len()); + for (b, e) in buffer.iter().zip(expected.iter()) { + assert_eq!(b, e); + } + } + + #[test] + fn test_hc128_clone() { + let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv + let mut rng1 = Hc128Rng::from_seed(seed); + let mut rng2 = rng1.clone(); + for _ in 0..16 { + assert_eq!(rng1.next_u32(), rng2.next_u32()); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prng/isaac.rs b/crates/rand-0.5.0-pre.2/src/prng/isaac.rs new file mode 100644 index 0000000..db4a736 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/isaac.rs @@ -0,0 +1,486 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The ISAAC random number generator. + +use core::{fmt, slice}; +use core::num::Wrapping as w; +use rand_core::{RngCore, SeedableRng, Error, le}; +use rand_core::block::{BlockRngCore, BlockRng}; +use prng::isaac_array::IsaacArray; + +#[allow(non_camel_case_types)] +type w32 = w<u32>; + +const RAND_SIZE_LEN: usize = 8; +const RAND_SIZE: usize = 1 << RAND_SIZE_LEN; + +/// A random number generator that uses the ISAAC algorithm. +/// +/// ISAAC stands for "Indirection, Shift, Accumulate, Add, and Count" which are +/// the principal bitwise operations employed. It is the most advanced of a +/// series of array based random number generator designed by Robert Jenkins +/// in 1996[1][2]. +/// +/// ISAAC is notably fast and produces excellent quality random numbers for +/// non-cryptographic applications. +/// +/// In spite of being designed with cryptographic security in mind, ISAAC hasn't +/// been stringently cryptanalyzed and thus cryptographers do not not +/// consensually trust it to be secure. When looking for a secure RNG, prefer +/// [`Hc128Rng`] instead, which, like ISAAC, is an array-based RNG and one of +/// the stream-ciphers selected the by eSTREAM contest. +/// +/// In 2006 an improvement to ISAAC was suggested by Jean-Philippe Aumasson, +/// named ISAAC+[3]. But because the specification is not complete, because +/// there is no good implementation, and because the suggested bias may not +/// exist, it is not implemented here. +/// +/// ## Overview of the ISAAC algorithm: +/// (in pseudo-code) +/// +/// ```text +/// Input: a, b, c, s[256] // state +/// Output: r[256] // results +/// +/// mix(a,i) = a ^ a << 13 if i = 0 mod 4 +/// a ^ a >> 6 if i = 1 mod 4 +/// a ^ a << 2 if i = 2 mod 4 +/// a ^ a >> 16 if i = 3 mod 4 +/// +/// c = c + 1 +/// b = b + c +/// +/// for i in 0..256 { +/// x = s_[i] +/// a = f(a,i) + s[i+128 mod 256] +/// y = a + b + s[x>>2 mod 256] +/// s[i] = y +/// b = x + s[y>>10 mod 256] +/// r[i] = b +/// } +/// ``` +/// +/// Numbers are generated in blocks of 256. This means the function above only +/// runs once every 256 times you ask for a next random number. In all other +/// circumstances the last element of the results array is returned. +/// +/// ISAAC therefore needs a lot of memory, relative to other non-crypto RNGs. +/// 2 * 256 * 4 = 2 kb to hold the state and results. +/// +/// This implementation uses [`BlockRng`] to implement the [`RngCore`] methods. +/// +/// ## References +/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number generator*]( +/// http://burtleburtle.net/bob/rand/isaacafa.html) +/// +/// [2]: Bob Jenkins, [*ISAAC and RC4*]( +/// http://burtleburtle.net/bob/rand/isaac.html) +/// +/// [3]: Jean-Philippe Aumasson, [*On the pseudo-random generator ISAAC*]( +/// https://eprint.iacr.org/2006/438) +/// +/// [`Hc128Rng`]: ../hc128/struct.Hc128Rng.html +/// [`BlockRng`]: ../../../rand_core/block/struct.BlockRng.html +/// [`RngCore`]: ../../trait.RngCore.html +#[derive(Clone, Debug)] +#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))] +pub struct IsaacRng(BlockRng<IsaacCore>); + +impl RngCore for IsaacRng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for IsaacRng { + type Seed = <IsaacCore as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + IsaacRng(BlockRng::<IsaacCore>::from_seed(seed)) + } + + fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> { + BlockRng::<IsaacCore>::from_rng(rng).map(|rng| IsaacRng(rng)) + } +} + +impl IsaacRng { + /// Create an ISAAC random number generator using the default + /// fixed seed. + /// + /// DEPRECATED. `IsaacRng::new_from_u64(0)` will produce identical results. + #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")] + pub fn new_unseeded() -> Self { + Self::new_from_u64(0) + } + + /// Create an ISAAC random number generator using an `u64` as seed. + /// If `seed == 0` this will produce the same stream of random numbers as + /// the reference implementation when used unseeded. + pub fn new_from_u64(seed: u64) -> Self { + IsaacRng(BlockRng::new(IsaacCore::new_from_u64(seed))) + } +} + +/// The core of `IsaacRng`, used with `BlockRng`. +#[derive(Clone)] +#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))] +pub struct IsaacCore { + #[cfg_attr(feature="serde1",serde(with="super::isaac_array::isaac_array_serde"))] + mem: [w32; RAND_SIZE], + a: w32, + b: w32, + c: w32, +} + +// Custom Debug implementation that does not expose the internal state +impl fmt::Debug for IsaacCore { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!(f, "IsaacCore {{}}") + } +} + +impl BlockRngCore for IsaacCore { + type Item = u32; + type Results = IsaacArraySelf::Item; + + /// Refills the output buffer, `results`. See also the pseudocode desciption + /// of the algorithm in the [`IsaacRng`] documentation. + /// + /// Optimisations used (similar to the reference implementation): + /// + /// - The loop is unrolled 4 times, once for every constant of mix(). + /// - The contents of the main loop are moved to a function `rngstep`, to + /// reduce code duplication. + /// - We use local variables for a and b, which helps with optimisations. + /// - We split the main loop in two, one that operates over 0..128 and one + /// over 128..256. This way we can optimise out the addition and modulus + /// from `s[i+128 mod 256]`. + /// - We maintain one index `i` and add `m` or `m2` as base (m2 for the + /// `s[i+128 mod 256]`), relying on the optimizer to turn it into pointer + /// arithmetic. + /// - We fill `results` backwards. The reference implementation reads values + /// from `results` in reverse. We read them in the normal direction, to + /// make `fill_bytes` a memcopy. To maintain compatibility we fill in + /// reverse. + /// + /// [`IsaacRng`]: struct.IsaacRng.html + fn generate(&mut self, results: &mut IsaacArraySelf::Item) { + self.c += w(1); + // abbreviations + let mut a = self.a; + let mut b = self.b + self.c; + const MIDPOINT: usize = RAND_SIZE / 2; + + #[inline] + fn ind(mem:&[w32; RAND_SIZE], v: w32, amount: usize) -> w32 { + let index = (v >> amount).0 as usize % RAND_SIZE; + mem[index] + } + + #[inline] + fn rngstep(mem: &mut [w32; RAND_SIZE], + results: &mut [u32; RAND_SIZE], + mix: w32, + a: &mut w32, + b: &mut w32, + base: usize, + m: usize, + m2: usize) { + let x = mem[base + m]; + *a = mix + mem[base + m2]; + let y = *a + *b + ind(&mem, x, 2); + mem[base + m] = y; + *b = x + ind(&mem, y, 2 + RAND_SIZE_LEN); + results[RAND_SIZE - 1 - base - m] = (*b).0; + } + + let mut m = 0; + let mut m2 = MIDPOINT; + for i in (0..MIDPOINT/4).map(|i| i * 4) { + rngstep(&mut self.mem, results, a ^ (a << 13), &mut a, &mut b, i + 0, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 6 ), &mut a, &mut b, i + 1, m, m2); + rngstep(&mut self.mem, results, a ^ (a << 2 ), &mut a, &mut b, i + 2, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 16), &mut a, &mut b, i + 3, m, m2); + } + + m = MIDPOINT; + m2 = 0; + for i in (0..MIDPOINT/4).map(|i| i * 4) { + rngstep(&mut self.mem, results, a ^ (a << 13), &mut a, &mut b, i + 0, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 6 ), &mut a, &mut b, i + 1, m, m2); + rngstep(&mut self.mem, results, a ^ (a << 2 ), &mut a, &mut b, i + 2, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 16), &mut a, &mut b, i + 3, m, m2); + } + + self.a = a; + self.b = b; + } +} + +impl IsaacCore { + /// Create a new ISAAC random number generator. + /// + /// The author Bob Jenkins describes how to best initialize ISAAC here: + /// https://rt.cpan.org/Public/Bug/Display.html?id=64324 + /// The answer is included here just in case: + /// + /// "No, you don't need a full 8192 bits of seed data. Normal key sizes will + /// do fine, and they should have their expected strength (eg a 40-bit key + /// will take as much time to brute force as 40-bit keys usually will). You + /// could fill the remainder with 0, but set the last array element to the + /// length of the key provided (to distinguish keys that differ only by + /// different amounts of 0 padding). You do still need to call randinit() to + /// make sure the initial state isn't uniform-looking." + /// "After publishing ISAAC, I wanted to limit the key to half the size of + /// r[], and repeat it twice. That would have made it hard to provide a key + /// that sets the whole internal state to anything convenient. But I'd + /// already published it." + /// + /// And his answer to the question "For my code, would repeating the key + /// over and over to fill 256 integers be a better solution than + /// zero-filling, or would they essentially be the same?": + /// "If the seed is under 32 bytes, they're essentially the same, otherwise + /// repeating the seed would be stronger. randinit() takes a chunk of 32 + /// bytes, mixes it, and combines that with the next 32 bytes, et cetera. + /// Then loops over all the elements the same way a second time." + #[inline] + fn init(mut mem: [w32; RAND_SIZE], rounds: u32) -> Self { + fn mix(a: &mut w32, b: &mut w32, c: &mut w32, d: &mut w32, + e: &mut w32, f: &mut w32, g: &mut w32, h: &mut w32) { + *a ^= *b << 11; *d += *a; *b += *c; + *b ^= *c >> 2; *e += *b; *c += *d; + *c ^= *d << 8; *f += *c; *d += *e; + *d ^= *e >> 16; *g += *d; *e += *f; + *e ^= *f << 10; *h += *e; *f += *g; + *f ^= *g >> 4; *a += *f; *g += *h; + *g ^= *h << 8; *b += *g; *h += *a; + *h ^= *a >> 9; *c += *h; *a += *b; + } + + // These numbers are the result of initializing a...h with the + // fractional part of the golden ratio in binary (0x9e3779b9) + // and applying mix() 4 times. + let mut a = w(0x1367df5a); + let mut b = w(0x95d90059); + let mut c = w(0xc3163e4b); + let mut d = w(0x0f421ad8); + let mut e = w(0xd92a4a78); + let mut f = w(0xa51a3c49); + let mut g = w(0xc4efea1b); + let mut h = w(0x30609119); + + // Normally this should do two passes, to make all of the seed effect + // all of `mem` + for _ in 0..rounds { + for i in (0..RAND_SIZE/8).map(|i| i * 8) { + a += mem[i ]; b += mem[i+1]; + c += mem[i+2]; d += mem[i+3]; + e += mem[i+4]; f += mem[i+5]; + g += mem[i+6]; h += mem[i+7]; + mix(&mut a, &mut b, &mut c, &mut d, + &mut e, &mut f, &mut g, &mut h); + mem[i ] = a; mem[i+1] = b; + mem[i+2] = c; mem[i+3] = d; + mem[i+4] = e; mem[i+5] = f; + mem[i+6] = g; mem[i+7] = h; + } + } + + Self { mem, a: w(0), b: w(0), c: w(0) } + } + + /// Create an ISAAC random number generator using an `u64` as seed. + /// If `seed == 0` this will produce the same stream of random numbers as + /// the reference implementation when used unseeded. + fn new_from_u64(seed: u64) -> Self { + let mut key = [w(0); RAND_SIZE]; + key[0] = w(seed as u32); + key[1] = w((seed >> 32) as u32); + // Initialize with only one pass. + // A second pass does not improve the quality here, because all of the + // seed was already available in the first round. + // Not doing the second pass has the small advantage that if + // `seed == 0` this method produces exactly the same state as the + // reference implementation when used unseeded. + Self::init(key, 1) + } +} + +impl SeedableRng for IsaacCore { + type Seed = [u8; 32]; + + fn from_seed(seed: Self::Seed) -> Self { + let mut seed_u32 = [0u32; 8]; + le::read_u32_into(&seed, &mut seed_u32); + // Convert the seed to `Wrapping<u32>` and zero-extend to `RAND_SIZE`. + let mut seed_extended = [w(0); RAND_SIZE]; + for (x, y) in seed_extended.iter_mut().zip(seed_u32.iter()) { + *x = w(*y); + } + Self::init(seed_extended, 2) + } + + fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> { + // Custom `from_rng` implementation that fills a seed with the same size + // as the entire state. + let mut seed = [w(0u32); RAND_SIZE]; + unsafe { + let ptr = seed.as_mut_ptr() as *mut u8; + + let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE * 4); + rng.try_fill_bytes(slice)?; + } + for i in seed.iter_mut() { + *i = w(i.0.to_le()); + } + + Ok(Self::init(seed, 2)) + } +} + +#[cfg(test)] +mod test { + use {RngCore, SeedableRng}; + use super::IsaacRng; + + #[test] + fn test_isaac_construction() { + // Test that various construction techniques produce a working RNG. + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng1 = IsaacRng::from_seed(seed); + assert_eq!(rng1.next_u32(), 2869442790); + + let mut rng2 = IsaacRng::from_rng(rng1).unwrap(); + assert_eq!(rng2.next_u32(), 3094074039); + } + + #[test] + fn test_isaac_true_values_32() { + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng1 = IsaacRng::from_seed(seed); + let mut results = [0u32; 10]; + for i in results.iter_mut() { *i = rng1.next_u32(); } + let expected = [ + 2558573138, 873787463, 263499565, 2103644246, 3595684709, + 4203127393, 264982119, 2765226902, 2737944514, 3900253796]; + assert_eq!(results, expected); + + let seed = [57,48,0,0, 50,9,1,0, 49,212,0,0, 148,38,0,0, + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng2 = IsaacRng::from_seed(seed); + // skip forward to the 10000th number + for _ in 0..10000 { rng2.next_u32(); } + + for i in results.iter_mut() { *i = rng2.next_u32(); } + let expected = [ + 3676831399, 3183332890, 2834741178, 3854698763, 2717568474, + 1576568959, 3507990155, 179069555, 141456972, 2478885421]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac_true_values_64() { + // As above, using little-endian versions of above values + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng = IsaacRng::from_seed(seed); + let mut results = [0u64; 5]; + for i in results.iter_mut() { *i = rng.next_u64(); } + let expected = [ + 3752888579798383186, 9035083239252078381,18052294697452424037, + 11876559110374379111, 16751462502657800130]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac_true_bytes() { + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng = IsaacRng::from_seed(seed); + let mut results = [0u8; 32]; + rng.fill_bytes(&mut results); + // Same as first values in test_isaac_true_values as bytes in LE order + let expected = [82, 186, 128, 152, 71, 240, 20, 52, + 45, 175, 180, 15, 86, 16, 99, 125, + 101, 203, 81, 214, 97, 162, 134, 250, + 103, 78, 203, 15, 150, 3, 210, 164]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac_new_uninitialized() { + // Compare the results from initializing `IsaacRng` with + // `new_from_u64(0)`, to make sure it is the same as the reference + // implementation when used uninitialized. + // Note: We only test the first 16 integers, not the full 256 of the + // first block. + let mut rng = IsaacRng::new_from_u64(0); + let mut results = [0u32; 16]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected: [u32; 16] = [ + 0x71D71FD2, 0xB54ADAE7, 0xD4788559, 0xC36129FA, + 0x21DC1EA9, 0x3CB879CA, 0xD83B237F, 0xFA3CE5BD, + 0x8D048509, 0xD82E9489, 0xDB452848, 0xCA20E846, + 0x500F972E, 0x0EEFF940, 0x00D6B993, 0xBC12C17F]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac_clone() { + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng1 = IsaacRng::from_seed(seed); + let mut rng2 = rng1.clone(); + for _ in 0..16 { + assert_eq!(rng1.next_u32(), rng2.next_u32()); + } + } + + #[test] + #[cfg(all(feature="serde1", feature="std"))] + fn test_isaac_serde() { + use bincode; + use std::io::{BufWriter, BufReader}; + + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng = IsaacRng::from_seed(seed); + + let buf: Vec<u8> = Vec::new(); + let mut buf = BufWriter::new(buf); + bincode::serialize_into(&mut buf, &rng).expect("Could not serialize"); + + let buf = buf.into_inner().unwrap(); + let mut read = BufReader::new(&buf[..]); + let mut deserialized: IsaacRng = bincode::deserialize_from(&mut read).expect("Could not deserialize"); + + for _ in 0..300 { // more than the 256 buffered results + assert_eq!(rng.next_u32(), deserialized.next_u32()); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prng/isaac64.rs b/crates/rand-0.5.0-pre.2/src/prng/isaac64.rs new file mode 100644 index 0000000..e922862 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/isaac64.rs @@ -0,0 +1,478 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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::{fmt, slice}; +use core::num::Wrapping as w; +use rand_core::{RngCore, SeedableRng, Error, le}; +use rand_core::block::{BlockRngCore, BlockRng64}; +use prng::isaac_array::IsaacArray; + +#[allow(non_camel_case_types)] +type w64 = w<u64>; + +const RAND_SIZE_LEN: usize = 8; +const RAND_SIZE: usize = 1 << RAND_SIZE_LEN; + +/// A random number generator that uses ISAAC-64, the 64-bit variant of the +/// ISAAC algorithm. +/// +/// ISAAC stands for "Indirection, Shift, Accumulate, Add, and Count" which are +/// the principal bitwise operations employed. It is the most advanced of a +/// series of array based random number generator designed by Robert Jenkins +/// in 1996[1]. +/// +/// ISAAC-64 is mostly similar to ISAAC. Because it operates on 64-bit integers +/// instead of 32-bit, it uses twice as much memory to hold its state and +/// results. Also it uses different constants for shifts and indirect indexing, +/// optimized to give good results for 64bit arithmetic. +/// +/// ISAAC-64 is notably fast and produces excellent quality random numbers for +/// non-cryptographic applications. +/// +/// In spite of being designed with cryptographic security in mind, ISAAC hasn't +/// been stringently cryptanalyzed and thus cryptographers do not not +/// consensually trust it to be secure. When looking for a secure RNG, prefer +/// [`Hc128Rng`] instead, which, like ISAAC, is an array-based RNG and one of +/// the stream-ciphers selected the by eSTREAM contest. +/// +/// ## Overview of the ISAAC-64 algorithm: +/// (in pseudo-code) +/// +/// ```text +/// Input: a, b, c, s[256] // state +/// Output: r[256] // results +/// +/// mix(a,i) = !(a ^ a << 21) if i = 0 mod 4 +/// a ^ a >> 5 if i = 1 mod 4 +/// a ^ a << 12 if i = 2 mod 4 +/// a ^ a >> 33 if i = 3 mod 4 +/// +/// c = c + 1 +/// b = b + c +/// +/// for i in 0..256 { +/// x = s_[i] +/// a = mix(a,i) + s[i+128 mod 256] +/// y = a + b + s[x>>3 mod 256] +/// s[i] = y +/// b = x + s[y>>11 mod 256] +/// r[i] = b +/// } +/// ``` +/// +/// This implementation uses [`BlockRng64`] to implement the [`RngCore`] methods. +/// +/// See for more information the documentation of [`IsaacRng`]. +/// +/// [1]: Bob Jenkins, [*ISAAC and RC4*]( +/// http://burtleburtle.net/bob/rand/isaac.html) +/// +/// [`IsaacRng`]: ../isaac/struct.IsaacRng.html +/// [`Hc128Rng`]: ../hc128/struct.Hc128Rng.html +/// [`BlockRng64`]: ../../../rand_core/block/struct.BlockRng64.html +/// [`RngCore`]: ../../trait.RngCore.html +#[derive(Clone, Debug)] +#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))] +pub struct Isaac64Rng(BlockRng64<Isaac64Core>); + +impl RngCore for Isaac64Rng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for Isaac64Rng { + type Seed = <Isaac64Core as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + Isaac64Rng(BlockRng64::<Isaac64Core>::from_seed(seed)) + } + + fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> { + BlockRng64::<Isaac64Core>::from_rng(rng).map(|rng| Isaac64Rng(rng)) + } +} + +impl Isaac64Rng { + /// Create a 64-bit ISAAC random number generator using the + /// default fixed seed. + /// + /// DEPRECATED. `Isaac64Rng::new_from_u64(0)` will produce identical results. + #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")] + pub fn new_unseeded() -> Self { + Self::new_from_u64(0) + } + + /// Create an ISAAC-64 random number generator using an `u64` as seed. + /// If `seed == 0` this will produce the same stream of random numbers as + /// the reference implementation when used unseeded. + pub fn new_from_u64(seed: u64) -> Self { + Isaac64Rng(BlockRng64::new(Isaac64Core::new_from_u64(seed))) + } +} + +/// The core of `Isaac64Rng`, used with `BlockRng`. +#[derive(Clone)] +#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))] +pub struct Isaac64Core { + #[cfg_attr(feature="serde1",serde(with="super::isaac_array::isaac_array_serde"))] + mem: [w64; RAND_SIZE], + a: w64, + b: w64, + c: w64, +} + +// Custom Debug implementation that does not expose the internal state +impl fmt::Debug for Isaac64Core { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!(f, "Isaac64Core {{}}") + } +} + +impl BlockRngCore for Isaac64Core { + type Item = u64; + type Results = IsaacArraySelf::Item; + + /// Refills the output buffer, `results`. See also the pseudocode desciption + /// of the algorithm in the [`Isaac64Rng`] documentation. + /// + /// Optimisations used (similar to the reference implementation): + /// + /// - The loop is unrolled 4 times, once for every constant of mix(). + /// - The contents of the main loop are moved to a function `rngstep`, to + /// reduce code duplication. + /// - We use local variables for a and b, which helps with optimisations. + /// - We split the main loop in two, one that operates over 0..128 and one + /// over 128..256. This way we can optimise out the addition and modulus + /// from `s[i+128 mod 256]`. + /// - We maintain one index `i` and add `m` or `m2` as base (m2 for the + /// `s[i+128 mod 256]`), relying on the optimizer to turn it into pointer + /// arithmetic. + /// - We fill `results` backwards. The reference implementation reads values + /// from `results` in reverse. We read them in the normal direction, to + /// make `fill_bytes` a memcopy. To maintain compatibility we fill in + /// reverse. + /// + /// [`Isaac64Rng`]: struct.Isaac64Rng.html + fn generate(&mut self, results: &mut IsaacArraySelf::Item) { + self.c += w(1); + // abbreviations + let mut a = self.a; + let mut b = self.b + self.c; + const MIDPOINT: usize = RAND_SIZE / 2; + + #[inline] + fn ind(mem:&[w64; RAND_SIZE], v: w64, amount: usize) -> w64 { + let index = (v >> amount).0 as usize % RAND_SIZE; + mem[index] + } + + #[inline] + fn rngstep(mem: &mut [w64; RAND_SIZE], + results: &mut [u64; RAND_SIZE], + mix: w64, + a: &mut w64, + b: &mut w64, + base: usize, + m: usize, + m2: usize) { + let x = mem[base + m]; + *a = mix + mem[base + m2]; + let y = *a + *b + ind(&mem, x, 3); + mem[base + m] = y; + *b = x + ind(&mem, y, 3 + RAND_SIZE_LEN); + results[RAND_SIZE - 1 - base - m] = (*b).0; + } + + let mut m = 0; + let mut m2 = MIDPOINT; + for i in (0..MIDPOINT/4).map(|i| i * 4) { + rngstep(&mut self.mem, results, !(a ^ (a << 21)), &mut a, &mut b, i + 0, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 5 ), &mut a, &mut b, i + 1, m, m2); + rngstep(&mut self.mem, results, a ^ (a << 12), &mut a, &mut b, i + 2, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 33), &mut a, &mut b, i + 3, m, m2); + } + + m = MIDPOINT; + m2 = 0; + for i in (0..MIDPOINT/4).map(|i| i * 4) { + rngstep(&mut self.mem, results, !(a ^ (a << 21)), &mut a, &mut b, i + 0, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 5 ), &mut a, &mut b, i + 1, m, m2); + rngstep(&mut self.mem, results, a ^ (a << 12), &mut a, &mut b, i + 2, m, m2); + rngstep(&mut self.mem, results, a ^ (a >> 33), &mut a, &mut b, i + 3, m, m2); + } + + self.a = a; + self.b = b; + } +} + +impl Isaac64Core { + /// Create a new ISAAC-64 random number generator. + fn init(mut mem: [w64; RAND_SIZE], rounds: u32) -> Self { + fn mix(a: &mut w64, b: &mut w64, c: &mut w64, d: &mut w64, + e: &mut w64, f: &mut w64, g: &mut w64, h: &mut w64) { + *a -= *e; *f ^= *h >> 9; *h += *a; + *b -= *f; *g ^= *a << 9; *a += *b; + *c -= *g; *h ^= *b >> 23; *b += *c; + *d -= *h; *a ^= *c << 15; *c += *d; + *e -= *a; *b ^= *d >> 14; *d += *e; + *f -= *b; *c ^= *e << 20; *e += *f; + *g -= *c; *d ^= *f >> 17; *f += *g; + *h -= *d; *e ^= *g << 14; *g += *h; + } + + // These numbers are the result of initializing a...h with the + // fractional part of the golden ratio in binary (0x9e3779b97f4a7c13) + // and applying mix() 4 times. + let mut a = w(0x647c4677a2884b7c); + let mut b = w(0xb9f8b322c73ac862); + let mut c = w(0x8c0ea5053d4712a0); + let mut d = w(0xb29b2e824a595524); + let mut e = w(0x82f053db8355e0ce); + let mut f = w(0x48fe4a0fa5a09315); + let mut g = w(0xae985bf2cbfc89ed); + let mut h = w(0x98f5704f6c44c0ab); + + // Normally this should do two passes, to make all of the seed effect + // all of `mem` + for _ in 0..rounds { + for i in (0..RAND_SIZE/8).map(|i| i * 8) { + a += mem[i ]; b += mem[i+1]; + c += mem[i+2]; d += mem[i+3]; + e += mem[i+4]; f += mem[i+5]; + g += mem[i+6]; h += mem[i+7]; + mix(&mut a, &mut b, &mut c, &mut d, + &mut e, &mut f, &mut g, &mut h); + mem[i ] = a; mem[i+1] = b; + mem[i+2] = c; mem[i+3] = d; + mem[i+4] = e; mem[i+5] = f; + mem[i+6] = g; mem[i+7] = h; + } + } + + Self { mem, a: w(0), b: w(0), c: w(0) } + } + + /// Create an ISAAC-64 random number generator using an `u64` as seed. + /// If `seed == 0` this will produce the same stream of random numbers as + /// the reference implementation when used unseeded. + pub fn new_from_u64(seed: u64) -> Self { + let mut key = [w(0); RAND_SIZE]; + key[0] = w(seed); + // Initialize with only one pass. + // A second pass does not improve the quality here, because all of the + // seed was already available in the first round. + // Not doing the second pass has the small advantage that if + // `seed == 0` this method produces exactly the same state as the + // reference implementation when used unseeded. + Self::init(key, 1) + } +} + +impl SeedableRng for Isaac64Core { + type Seed = [u8; 32]; + + fn from_seed(seed: Self::Seed) -> Self { + let mut seed_u64 = [0u64; 4]; + le::read_u64_into(&seed, &mut seed_u64); + // Convert the seed to `Wrapping<u64>` and zero-extend to `RAND_SIZE`. + let mut seed_extended = [w(0); RAND_SIZE]; + for (x, y) in seed_extended.iter_mut().zip(seed_u64.iter()) { + *x = w(*y); + } + Self::init(seed_extended, 2) + } + + fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> { + // Custom `from_rng` implementation that fills a seed with the same size + // as the entire state. + let mut seed = [w(0u64); RAND_SIZE]; + unsafe { + let ptr = seed.as_mut_ptr() as *mut u8; + let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE * 8); + rng.try_fill_bytes(slice)?; + } + for i in seed.iter_mut() { + *i = w(i.0.to_le()); + } + + Ok(Self::init(seed, 2)) + } +} + +#[cfg(test)] +mod test { + use {RngCore, SeedableRng}; + use super::Isaac64Rng; + + #[test] + fn test_isaac64_construction() { + // Test that various construction techniques produce a working RNG. + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng1 = Isaac64Rng::from_seed(seed); + assert_eq!(rng1.next_u64(), 14964555543728284049); + + let mut rng2 = Isaac64Rng::from_rng(rng1).unwrap(); + assert_eq!(rng2.next_u64(), 919595328260451758); + } + + #[test] + fn test_isaac64_true_values_64() { + let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0, + 200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0]; + let mut rng1 = Isaac64Rng::from_seed(seed); + let mut results = [0u64; 10]; + for i in results.iter_mut() { *i = rng1.next_u64(); } + let expected = [ + 15071495833797886820, 7720185633435529318, + 10836773366498097981, 5414053799617603544, + 12890513357046278984, 17001051845652595546, + 9240803642279356310, 12558996012687158051, + 14673053937227185542, 1677046725350116783]; + assert_eq!(results, expected); + + let seed = [57,48,0,0, 0,0,0,0, 50,9,1,0, 0,0,0,0, + 49,212,0,0, 0,0,0,0, 148,38,0,0, 0,0,0,0]; + let mut rng2 = Isaac64Rng::from_seed(seed); + // skip forward to the 10000th number + for _ in 0..10000 { rng2.next_u64(); } + + for i in results.iter_mut() { *i = rng2.next_u64(); } + let expected = [ + 18143823860592706164, 8491801882678285927, 2699425367717515619, + 17196852593171130876, 2606123525235546165, 15790932315217671084, + 596345674630742204, 9947027391921273664, 11788097613744130851, + 10391409374914919106]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac64_true_values_32() { + let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0, + 200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0]; + let mut rng = Isaac64Rng::from_seed(seed); + let mut results = [0u32; 12]; + for i in results.iter_mut() { *i = rng.next_u32(); } + // Subset of above values, as an LE u32 sequence + let expected = [ + 3477963620, 3509106075, + 687845478, 1797495790, + 227048253, 2523132918, + 4044335064, 1260557630, + 4079741768, 3001306521, + 69157722, 3958365844]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac64_true_values_mixed() { + let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0, + 200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0]; + let mut rng = Isaac64Rng::from_seed(seed); + // Test alternating between `next_u64` and `next_u32` works as expected. + // Values are the same as `test_isaac64_true_values` and + // `test_isaac64_true_values_32`. + assert_eq!(rng.next_u64(), 15071495833797886820); + assert_eq!(rng.next_u32(), 687845478); + assert_eq!(rng.next_u32(), 1797495790); + assert_eq!(rng.next_u64(), 10836773366498097981); + assert_eq!(rng.next_u32(), 4044335064); + // Skip one u32 + assert_eq!(rng.next_u64(), 12890513357046278984); + assert_eq!(rng.next_u32(), 69157722); + } + + #[test] + fn test_isaac64_true_bytes() { + let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0, + 200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0]; + let mut rng = Isaac64Rng::from_seed(seed); + let mut results = [0u8; 32]; + rng.fill_bytes(&mut results); + // Same as first values in test_isaac64_true_values as bytes in LE order + let expected = [100, 131, 77, 207, 155, 181, 40, 209, + 102, 176, 255, 40, 238, 155, 35, 107, + 61, 123, 136, 13, 246, 243, 99, 150, + 216, 167, 15, 241, 62, 149, 34, 75]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac64_new_uninitialized() { + // Compare the results from initializing `IsaacRng` with + // `new_from_u64(0)`, to make sure it is the same as the reference + // implementation when used uninitialized. + // Note: We only test the first 16 integers, not the full 256 of the + // first block. + let mut rng = Isaac64Rng::new_from_u64(0); + let mut results = [0u64; 16]; + for i in results.iter_mut() { *i = rng.next_u64(); } + let expected: [u64; 16] = [ + 0xF67DFBA498E4937C, 0x84A5066A9204F380, 0xFEE34BD5F5514DBB, + 0x4D1664739B8F80D6, 0x8607459AB52A14AA, 0x0E78BC5A98529E49, + 0xFE5332822AD13777, 0x556C27525E33D01A, 0x08643CA615F3149F, + 0xD0771FAF3CB04714, 0x30E86F68A37B008D, 0x3074EBC0488A3ADF, + 0x270645EA7A2790BC, 0x5601A0A8D3763C6A, 0x2F83071F53F325DD, + 0xB9090F3D42D2D2EA]; + assert_eq!(results, expected); + } + + #[test] + fn test_isaac64_clone() { + let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0, + 200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0]; + let mut rng1 = Isaac64Rng::from_seed(seed); + let mut rng2 = rng1.clone(); + for _ in 0..16 { + assert_eq!(rng1.next_u64(), rng2.next_u64()); + } + } + + #[test] + #[cfg(all(feature="serde1", feature="std"))] + fn test_isaac64_serde() { + use bincode; + use std::io::{BufWriter, BufReader}; + + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng = Isaac64Rng::from_seed(seed); + + let buf: Vec<u8> = Vec::new(); + let mut buf = BufWriter::new(buf); + bincode::serialize_into(&mut buf, &rng).expect("Could not serialize"); + + let buf = buf.into_inner().unwrap(); + let mut read = BufReader::new(&buf[..]); + let mut deserialized: Isaac64Rng = bincode::deserialize_from(&mut read).expect("Could not deserialize"); + + for _ in 0..300 { // more than the 256 buffered results + assert_eq!(rng.next_u64(), deserialized.next_u64()); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prng/isaac_array.rs b/crates/rand-0.5.0-pre.2/src/prng/isaac_array.rs new file mode 100644 index 0000000..3ebf828 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/isaac_array.rs @@ -0,0 +1,137 @@ +// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! ISAAC helper functions for 256-element arrays. + +// Terrible workaround because arrays with more than 32 elements do not +// implement `AsRef`, `Default`, `Serialize`, `Deserialize`, or any other +// traits for that matter. + +#[cfg(feature="serde1")] use serde::{Serialize, Deserialize}; + +const RAND_SIZE_LEN: usize = 8; +const RAND_SIZE: usize = 1 << RAND_SIZE_LEN; + + +#[derive(Copy, Clone)] +#[allow(missing_debug_implementations)] +#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))] +pub struct IsaacArray<T> { + #[cfg_attr(feature="serde1",serde(with="isaac_array_serde"))] + #[cfg_attr(feature="serde1", serde(bound( + serialize = "T: Serialize", + deserialize = "T: Deserialize<'de> + Copy + Default")))] + inner: [T; RAND_SIZE] +} + +impl<T> ::core::convert::AsRef<[T]> for IsaacArray<T> { + #[inline(always)] + fn as_ref(&self) -> &[T] { + &self.inner[..] + } +} + +impl<T> ::core::convert::AsMut<[T]> for IsaacArray<T> { + #[inline(always)] + fn as_mut(&mut self) -> &mut [T] { + &mut self.inner[..] + } +} + +impl<T> ::core::ops::Deref for IsaacArray<T> { + type Target = [T; RAND_SIZE]; + #[inline(always)] + fn deref(&self) -> &Self::Target { + &self.inner + } +} + +impl<T> ::core::ops::DerefMut for IsaacArray<T> { + #[inline(always)] + fn deref_mut(&mut self) -> &mut [T; RAND_SIZE] { + &mut self.inner + } +} + +impl<T> ::core::default::Default for IsaacArray<T> where T: Copy + Default { + fn default() -> IsaacArray<T> { + IsaacArray { inner: [T::default(); RAND_SIZE] } + } +} + + +#[cfg(feature="serde1")] +pub(super) mod isaac_array_serde { + const RAND_SIZE_LEN: usize = 8; + const RAND_SIZE: usize = 1 << RAND_SIZE_LEN; + + use serde::{Deserialize, Deserializer, Serialize, Serializer}; + use serde::de::{Visitor,SeqAccess}; + use serde::de; + + use core::fmt; + + pub fn serialize<T, S>(arr: &[T;RAND_SIZE], ser: S) -> Result<S::Ok, S::Error> + where + T: Serialize, + S: Serializer + { + use serde::ser::SerializeTuple; + + let mut seq = ser.serialize_tuple(RAND_SIZE)?; + + for e in arr.iter() { + seq.serialize_element(&e)?; + } + + seq.end() + } + + #[inline] + pub fn deserialize<'de, T, D>(de: D) -> Result<[T;RAND_SIZE], D::Error> + where + T: Deserialize<'de>+Default+Copy, + D: Deserializer<'de>, + { + use core::marker::PhantomData; + struct ArrayVisitor<T> { + _pd: PhantomData<T>, + }; + impl<'de,T> Visitor<'de> for ArrayVisitor<T> + where + T: Deserialize<'de>+Default+Copy + { + type Value = [T; RAND_SIZE]; + + fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result { + formatter.write_str("Isaac state array") + } + + #[inline] + fn visit_seq<A>(self, mut seq: A) -> Result<[T; RAND_SIZE], A::Error> + where + A: SeqAccess<'de>, + { + let mut out = [Default::default();RAND_SIZE]; + + for i in 0..RAND_SIZE { + match seq.next_element()? { + Some(val) => out[i] = val, + None => return Err(de::Error::invalid_length(i, &self)), + }; + } + + Ok(out) + } + } + + de.deserialize_tuple(RAND_SIZE, ArrayVisitor{_pd: PhantomData}) + } +} diff --git a/crates/rand-0.5.0-pre.2/src/prng/mod.rs b/crates/rand-0.5.0-pre.2/src/prng/mod.rs new file mode 100644 index 0000000..240b682 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/mod.rs @@ -0,0 +1,330 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Pseudo-random number generators. +//! +//! Pseudo-random number generators are algorithms to produce apparently random +//! numbers deterministically, and usually fairly quickly. See the documentation +//! of the [`rngs` module] for some introduction to PRNGs. +//! +//! As mentioned there, PRNGs fall in two broad categories: +//! +//! - [basic PRNGs], primarily designed for simulations +//! - [CSPRNGs], primarily designed for cryptography +//! +//! In simple terms, the basic PRNGs are often predictable; CSPRNGs should not +//! be predictable *when used correctly*. +//! +//! Contents of this documentation: +//! +//! 1. [The generators](#the-generators) +//! 1. [Performance and size](#performance) +//! 1. [Quality and cycle length](#quality) +//! 1. [Security](#security) +//! 1. [Extra features](#extra-features) +//! 1. [Further reading](#further-reading) +//! +//! +//! # The generators +//! +//! ## Basic pseudo-random number generators (PRNGs) +//! +//! The goal of regular, non-cryptographic PRNGs is usually to find a good +//! balance between simplicity, quality, memory usage and performance. These +//! algorithms are very important to Monte Carlo simulations, and also suitable +//! for several other problems such as randomized algorithms and games (except +//! where there is a risk of players predicting the next output value from +//! previous values, in which case a CSPRNG should be used). +//! +//! Currently Rand provides only one PRNG, and not a very good one at that: +//! +//! | name | full name | performance | memory | quality | period | features | +//! |------|-----------|-------------|--------|---------|--------|----------| +//! | [`XorShiftRng`] | Xorshift 32/128 | ★★★☆☆ | 16 bytes | ★☆☆☆☆ | `u32` * 2¹²⁸ - 1 | — | +//! +// Quality stars [not rendered in documentation]: +// 5. reserved for crypto-level (e.g. ChaCha8, ISAAC) +// 4. good performance on TestU01 and PractRand, good theory +// (e.g. PCG, truncated Xorshift*) +// 3. good performance on TestU01 and PractRand, but "falling through the +// cracks" or insufficient theory (e.g. SFC, Xoshiro) +// 2. imperfect performance on tests or other limiting properties, but not +// terrible (e.g. Xoroshiro128+) +// 1. clear deficiencies in test results, cycle length, theory, or other +// properties (e.g. Xorshift) +// +// Performance stars [not rendered in documentation]: +// Meant to give an indication of relative performance. Roughly follows a log +// scale, based on the performance of `next_u64` on a current i5/i7: +// - 5. 8000 MB/s+ +// - 4. 4000 MB/s+ +// - 3. 2000 MB/s+ +// - 2. 1000 MB/s+ +// - 1. < 1000 MB/s +// +//! ## Cryptographically secure pseudo-random number generators (CSPRNGs) +//! +//! CSPRNGs have much higher requirements than basic PRNGs. The primary +//! consideration is security. Performance and simplicity are also important, +//! but in general CSPRNGs are more complex and slower than regular PRNGs. +//! Quality is no longer a concern, as it is a requirement for a +//! CSPRNG that the output is basically indistinguishable from true randomness +//! since any bias or correlation makes the output more predictable. +//! +//! There is a close relationship between CSPRNGs and cryptographic ciphers. +//! Any block cipher can be turned into a CSPRNG by encrypting a counter. Stream +//! ciphers are basically a CSPRNG and a combining operation, usually XOR. This +//! means that we can easily use any stream cipher as a CSPRNG. +//! +//! Rand currently provides two trustworthy CSPRNGs and two CSPRNG-like PRNGs: +//! +//! | name | full name | performance | initialization | memory | predictability | forward secrecy | +//! |------|-----------|--------------|--------------|----------|----------------|-------------------------| +//! | [`ChaChaRng`] | ChaCha20 | ★☆☆☆☆ | fast | 136 bytes | secure | no | +//! | [`Hc128Rng`] | HC-128 | ★★☆☆☆ | slow | 4176 bytes | secure | no | +//! | [`IsaacRng`] | ISAAC | ★★☆☆☆ | slow | 2072 bytes | unknown | unknown | +//! | [`Isaac64Rng`] | ISAAC-64 | ★★☆☆☆ | slow | 4136 bytes| unknown | unknown | +//! +//! It should be noted that the ISAAC generators are only included for +//! historical reasons, they have been with the Rust language since the very +//! beginning. They have good quality output and no attacks are known, but have +//! received little attention from cryptography experts. +//! +//! +//! # Performance +//! +//! First it has to be said most PRNGs are very fast, and will rarely be a +//! performance bottleneck. +//! +//! Performance of basic PRNGs is a bit of a subtle thing. It depends a lot on +//! the CPU architecture (32 vs. 64 bits), inlining, and also on the number of +//! available registers. This often causes the performance to be affected by +//! surrounding code due to inlining and other usage of registers. +//! +//! When choosing a PRNG for performance it is important to benchmark your own +//! application due to interactions between PRNGs and surrounding code and +//! dependence on the CPU architecture as well as the impact of the size of +//! data requested. Because of all this, we do not include performance numbers +//! here but merely a qualitative rating. +//! +//! CSPRNGs are a little different in that they typically generate a block of +//! output in a cache, and pull outputs from the cache. This allows them to have +//! good amortised performance, and reduces or completely removes the influence +//! of surrounding code on the CSPRNG performance. +//! +//! ### Worst-case performance +//! Because CSPRNGs usually produce a block of values into a cache, they have +//! poor worst case performance (in contrast to basic PRNGs, where the +//! performance is usually quite regular). +//! +//! ## State size +//! +//! Simple PRNGs often use very little memory, commonly only a few words, where +//! a *word* is usually either `u32` or `u64`. This is not true for all +//! non-cryptographic PRNGs however, for example the historically popular +//! Mersenne Twister MT19937 algorithm requires 2.5 kB of state. +//! +//! CSPRNGs typically require more memory; since the seed size is recommended +//! to be at least 192 bits and some more may be required for the algorithm, +//! 256 bits would be approximately the minimum secure size. In practice, +//! CSPRNGs tend to use quite a bit more, [`ChaChaRng`] is relatively small with +//! 136 bytes of state. +//! +//! ## Initialization time +//! +//! The time required to initialize new generators varies significantly. Many +//! simple PRNGs and even some cryptographic ones (including [`ChaChaRng`]) +//! only need to copy the seed value and some constants into their state, and +//! thus can be constructed very quickly. In contrast, CSPRNGs with large state +//! require an expensive key-expansion. +//! +//! # Quality +//! +//! Many basic PRNGs are not much more than a couple of bitwise and arithmetic +//! operations. Their simplicity gives good performance, but also means there +//! are small regularities hidden in the generated random number stream. +//! +//! How much do those hidden regularities matter? That is hard to say, and +//! depends on how the RNG gets used. If there happen to be correlations between +//! the random numbers and the algorithm they are used in, the results can be +//! wrong or misleading. +//! +//! A random number generator can be considered good if it gives the correct +//! results in as many applications as possible. The quality of PRNG +//! algorithms can be evaluated to some extend analytically, to determine the +//! cycle length and to rule out some correlations. Then there are empirical +//! test suites designed to test how well a PRNG performs on a wide range of +//! possible uses, the latest and most complete of which are [TestU01] and +//! [PractRand]. +//! +//! CSPRNGs tend to be more complex, and have an explicit requirement to be +//! unpredictable. This implies there must be no obvious correlations between +//! output values. +//! +//! ### Quality stars: +//! PRNGs with 3 stars or more should be good enough for any purpose. +//! 1 or 2 stars may be good enough for typical apps and games, but do not work +//! well with all algorithms. +//! +//! ## Period +//! +//! The *period* or *cycle length* of a PRNG is the number of values that can be +//! generated after which it starts repeating the same random number stream. +//! Many PRNGs have a fixed-size period, but for some only an expected average +//! cycle length can be given, where the exact length depends on the seed. +//! +//! On today's hardware, even a fast RNG with a cycle length of *only* +//! 2⁶⁴ can be used for centuries before cycling. Yet we recommend a +//! period of 2¹²⁸ or more, which most modern PRNGs satisfy. +//! Alternatively a PRNG with shorter period but support for multiple streams +//! may be chosen. There are two reasons for this, as follows. +//! +//! If we see the entire period of an RNG as one long random number stream, +//! every independently seeded RNG returns a slice of that stream. When multiple +//! RNG are seeded randomly, there is an increasingly large chance to end up +//! with a partially overlapping slice of the stream. +//! +//! If the period of the RNG is 2¹²⁸, and an application consumes +//! 2⁴⁸ values, it then takes about 2³² random +//! initializations to have a chance of 1 in a million to repeat part of an +//! already used stream. This seems good enough for common usage of +//! non-cryptographic generators, hence the recommendation of at least +//! 2¹²⁸. As an estimate, the chance of any overlap in a period of +//! size `p` with `n` independent seeds and `u` values used per seed is +//! approximately `1 - e^(-u * n^2 / (2 * p))`. +//! +//! Further, it is not recommended to use the full period of an RNG. Many +//! PRNGs have a property called *k-dimensional equidistribution*, meaning that +//! for values of some size (potentially larger than the output size), all +//! possible values are produced the same number of times over the generator's +//! period. This is not a property of true randomness. This is known as the +//! generalized birthday problem, see the [PCG paper] for a good explanation. +//! This results in a noticable bias on output after generating more values +//! than the square root of the period (after 2⁶⁴ values for a +//! period of 2¹²⁸). +//! +//! +//! # Security +//! +//! ## Predictability +//! +//! From the context of any PRNG, one can ask the question *given some previous +//! output from the PRNG, is it possible to predict the next output value?* +//! This is an important property in any situation where there might be an +//! adversary. +//! +//! Regular PRNGs tend to be predictable, although with varying difficulty. In +//! some cases prediction is trivial, for example plain Xorshift outputs part of +//! its state without mutation, and prediction is as simple as seeding a new +//! Xorshift generator from four `u32` outputs. Other generators, like +//! [PCG](http://www.pcg-random.org/predictability.html) and truncated Xorshift* +//! are harder to predict, but not outside the realm of common mathematics and a +//! desktop PC. +//! +//! The basic security that CSPRNGs must provide is the infeasibility to predict +//! output. This requirement is formalized as the [next-bit test]; this is +//! roughly stated as: given the first *k* bits of a random sequence, the +//! sequence satisfies the next-bit test if there is no algorithm able to +//! predict the next bit using reasonable computing power. +//! +//! A further security that *some* CSPRNGs provide is forward secrecy: +//! in the event that the CSPRNGs state is revealed at some point, it must be +//! infeasible to reconstruct previous states or output. Note that many CSPRNGs +//! *do not* have forward secrecy in their usual formulations. +//! +//! As an outsider it is hard to get a good idea about the security of an +//! algorithm. People in the field of cryptography spend a lot of effort +//! analyzing existing designs, and what was once considered good may now turn +//! out to be weaker. Generally it is best to use algorithms well-analyzed by +//! experts, such as those recommended by NIST or ECRYPT. +//! +//! ## State and seeding +//! +//! It is worth noting that a CSPRNG's security relies absolutely on being +//! seeded with a secure random key. Should the key be known or guessable, all +//! output of the CSPRNG is easy to guess. This implies that the seed should +//! come from a trusted source; usually either the OS or another CSPRNG. Our +//! seeding helper trait, [`FromEntropy`], and the source it uses +//! ([`EntropyRng`]), should be secure. Additionally, [`ThreadRng`] is a CSPRNG, +//! thus it is acceptable to seed from this (although for security applications +//! fresh/external entropy should be preferred). +//! +//! Further, it should be obvious that the internal state of a CSPRNG must be +//! kept secret. With that in mind, our implementations do not provide direct +//! access to most of their internal state, and `Debug` implementations do not +//! print any internal state. This does not fully protect CSPRNG state; code +//! within the same process may read this memory (and we allow cloning and +//! serialisation of CSPRNGs for convenience). Further, a running process may be +//! forked by the operating system, which may leave both processes with a copy +//! of the same generator. +//! +//! ## Not a crypto library +//! +//! It should be emphasised that this is not a cryptography library; although +//! Rand does take some measures to provide secure random numbers, it does not +//! necessarily take all recommended measures. Further, cryptographic processes +//! such as encryption and authentication are complex and must be implemented +//! very carefully to avoid flaws and resist known attacks. It is therefore +//! recommended to use specialized libraries where possible, for example +//! [openssl], [ring] and the [RustCrypto libraries]. +//! +//! +//! # Extra features +//! +//! Some PRNGs may provide extra features, like: +//! +//! - Support for multiple streams, which can help with parallel tasks. +//! - The ability to jump or seek around in the random number stream; +//! with large periood this can be used as an alternative to streams. +//! +//! +//! # Further reading +//! +//! There is quite a lot that can be said about PRNGs. The [PCG paper] is a +//! very approachable explaining more concepts. +//! +//! A good paper about RNG quality is +//! ["Good random number generators are (not so) easy to find"]( +//! http://random.mat.sbg.ac.at/results/peter/A19final.pdf) by P. Hellekalek. +//! +//! +//! [`rngs` module]: ../rngs/index.html +//! [basic PRNGs]: #basic-pseudo-random-number-generators-prngs +//! [CSPRNGs]: #cryptographically-secure-pseudo-random-number-generators-csprngs +//! [`XorShiftRng`]: struct.XorShiftRng.html +//! [`ChaChaRng`]: chacha/struct.ChaChaRng.html +//! [`Hc128Rng`]: hc128/struct.Hc128Rng.html +//! [`IsaacRng`]: isaac/struct.IsaacRng.html +//! [`Isaac64Rng`]: isaac64/struct.Isaac64Rng.html +//! [`ThreadRng`]: ../rngs/struct.ThreadRng.html +//! [`FromEntropy`]: ../trait.FromEntropy.html +//! [`EntropyRng`]: ../rngs/struct.EntropyRng.html +//! [TestU01]: http://simul.iro.umontreal.ca/testu01/tu01.html +//! [PractRand]: http://pracrand.sourceforge.net/ +//! [PCG paper]: http://www.pcg-random.org/pdf/hmc-cs-2014-0905.pdf +//! [openssl]: https://crates.io/crates/openssl +//! [ring]: https://crates.io/crates/ring +//! [RustCrypto libraries]: https://github.com/RustCrypto +//! [next-bit test]: https://en.wikipedia.org/wiki/Next-bit_test + + +pub mod chacha; +pub mod hc128; +pub mod isaac; +pub mod isaac64; +mod xorshift; + +mod isaac_array; + +pub use self::chacha::ChaChaRng; +pub use self::hc128::Hc128Rng; +pub use self::isaac::IsaacRng; +pub use self::isaac64::Isaac64Rng; +pub use self::xorshift::XorShiftRng; diff --git a/crates/rand-0.5.0-pre.2/src/prng/xorshift.rs b/crates/rand-0.5.0-pre.2/src/prng/xorshift.rs new file mode 100644 index 0000000..5f96170 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/prng/xorshift.rs @@ -0,0 +1,226 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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 core::{fmt, slice}; +use rand_core::{RngCore, SeedableRng, Error, impls, le}; + +/// 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"](https://www.jstatsoft.org/v08/i14/paper). *Journal of +/// Statistical Software*. Vol. 8 (Issue 14). +#[derive(Clone)] +#[cfg_attr(feature="serde1", derive(Serialize,Deserialize))] +pub struct XorShiftRng { + x: w<u32>, + y: w<u32>, + z: w<u32>, + w: w<u32>, +} + +// Custom Debug implementation that does not expose the internal state +impl fmt::Debug for XorShiftRng { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + write!(f, "XorShiftRng {{}}") + } +} + +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 + #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")] + pub fn new_unseeded() -> XorShiftRng { + XorShiftRng { + x: w(0x193a6754), + y: w(0xa8a7d469), + z: w(0x97830e05), + w: w(0x113ba7bb), + } + } +} + +impl RngCore 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 + } + + #[inline] + fn next_u64(&mut self) -> u64 { + impls::next_u64_via_u32(self) + } + + #[inline] + fn fill_bytes(&mut self, dest: &mut [u8]) { + impls::fill_bytes_via_next(self, dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + Ok(self.fill_bytes(dest)) + } +} + +impl SeedableRng for XorShiftRng { + type Seed = [u8; 16]; + + fn from_seed(seed: Self::Seed) -> Self { + let mut seed_u32 = [0u32; 4]; + le::read_u32_into(&seed, &mut seed_u32); + + // Xorshift cannot be seeded with 0 and we cannot return an Error, but + // also do not wish to panic (because a random seed can legitimately be + // 0); our only option is therefore to use a preset value. + if seed_u32.iter().all(|&x| x == 0) { + seed_u32 = [0xBAD_5EED, 0xBAD_5EED, 0xBAD_5EED, 0xBAD_5EED]; + } + + XorShiftRng { + x: w(seed_u32[0]), + y: w(seed_u32[1]), + z: w(seed_u32[2]), + w: w(seed_u32[3]), + } + } + + fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> { + let mut seed_u32 = [0u32; 4]; + loop { + unsafe { + let ptr = seed_u32.as_mut_ptr() as *mut u8; + + let slice = slice::from_raw_parts_mut(ptr, 4 * 4); + rng.try_fill_bytes(slice)?; + } + if !seed_u32.iter().all(|&x| x == 0) { break; } + } + + Ok(XorShiftRng { + x: w(seed_u32[0]), + y: w(seed_u32[1]), + z: w(seed_u32[2]), + w: w(seed_u32[3]), + }) + } +} + +#[cfg(test)] +mod tests { + use {RngCore, SeedableRng}; + use super::XorShiftRng; + + #[test] + fn test_xorshift_construction() { + // Test that various construction techniques produce a working RNG. + let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16]; + let mut rng1 = XorShiftRng::from_seed(seed); + assert_eq!(rng1.next_u64(), 4325440999699518727); + + let _rng2 = XorShiftRng::from_rng(rng1).unwrap(); + // Note: we cannot test the state of _rng2 because from_rng does not + // fix Endianness. This is allowed in the trait specification. + } + + #[test] + fn test_xorshift_true_values() { + let seed = [16,15,14,13, 12,11,10,9, 8,7,6,5, 4,3,2,1]; + let mut rng = XorShiftRng::from_seed(seed); + + let mut results = [0u32; 9]; + for i in results.iter_mut() { *i = rng.next_u32(); } + let expected: [u32; 9] = [ + 2081028795, 620940381, 269070770, 16943764, 854422573, 29242889, + 1550291885, 1227154591, 271695242]; + assert_eq!(results, expected); + + let mut results = [0u64; 9]; + for i in results.iter_mut() { *i = rng.next_u64(); } + let expected: [u64; 9] = [ + 9247529084182843387, 8321512596129439293, 14104136531997710878, + 6848554330849612046, 343577296533772213, 17828467390962600268, + 9847333257685787782, 7717352744383350108, 1133407547287910111]; + assert_eq!(results, expected); + + let mut results = [0u8; 32]; + rng.fill_bytes(&mut results); + let expected = [102, 57, 212, 16, 233, 130, 49, 183, + 158, 187, 44, 203, 63, 149, 45, 17, + 117, 129, 131, 160, 70, 121, 158, 155, + 224, 209, 192, 53, 10, 62, 57, 72]; + assert_eq!(results, expected); + } + + #[test] + fn test_xorshift_zero_seed() { + // Xorshift does not work with an all zero seed. + // Assert it does not panic. + let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng = XorShiftRng::from_seed(seed); + let a = rng.next_u64(); + let b = rng.next_u64(); + assert!(a != 0); + assert!(b != a); + } + + #[test] + fn test_xorshift_clone() { + let seed = [1,2,3,4, 5,5,7,8, 8,7,6,5, 4,3,2,1]; + let mut rng1 = XorShiftRng::from_seed(seed); + let mut rng2 = rng1.clone(); + for _ in 0..16 { + assert_eq!(rng1.next_u64(), rng2.next_u64()); + } + } + + #[cfg(all(feature="serde1", feature="std"))] + #[test] + fn test_xorshift_serde() { + use bincode; + use std::io::{BufWriter, BufReader}; + + let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16]; + let mut rng = XorShiftRng::from_seed(seed); + + let buf: Vec<u8> = Vec::new(); + let mut buf = BufWriter::new(buf); + bincode::serialize_into(&mut buf, &rng).expect("Could not serialize"); + + let buf = buf.into_inner().unwrap(); + let mut read = BufReader::new(&buf[..]); + let mut deserialized: XorShiftRng = bincode::deserialize_from(&mut read).expect("Could not deserialize"); + + assert_eq!(rng.x, deserialized.x); + assert_eq!(rng.y, deserialized.y); + assert_eq!(rng.z, deserialized.z); + assert_eq!(rng.w, deserialized.w); + + for _ in 0..16 { + assert_eq!(rng.next_u64(), deserialized.next_u64()); + } + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/adapter/mod.rs b/crates/rand-0.5.0-pre.2/src/rngs/adapter/mod.rs new file mode 100644 index 0000000..9a3851a --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/adapter/mod.rs @@ -0,0 +1,17 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Wrappers / adapters forming RNGs + +#[cfg(feature="std")] #[doc(hidden)] pub mod read; +mod reseeding; + +#[cfg(feature="std")] pub use self::read::ReadRng; +pub use self::reseeding::ReseedingRng; diff --git a/crates/rand-0.5.0-pre.2/src/rngs/adapter/read.rs b/crates/rand-0.5.0-pre.2/src/rngs/adapter/read.rs new file mode 100644 index 0000000..de75f97 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/adapter/read.rs @@ -0,0 +1,137 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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::Read; + +use rand_core::{RngCore, Error, ErrorKind, impls}; + + +/// An RNG that reads random bytes straight from any type supporting +/// `std::io::Read`, for example files. +/// +/// This will work best with an infinite reader, but that is not required. +/// +/// This can be used with `/dev/urandom` on Unix but it is recommended to use +/// [`OsRng`] instead. +/// +/// # Panics +/// +/// `ReadRng` uses `std::io::read_exact`, which retries on interrupts. All other +/// errors from the underlying reader, including when it does not have enough +/// data, will only be reported through [`try_fill_bytes`]. The other +/// [`RngCore`] methods will panic in case of an error. +/// +/// # Example +/// +/// ``` +/// 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>()); +/// ``` +/// +/// [`OsRng`]: ../struct.OsRng.html +/// [`RngCore`]: ../../trait.RngCore.html +/// [`try_fill_bytes`]: ../../trait.RngCore.html#method.tymethod.try_fill_bytes +#[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> RngCore for ReadRng<R> { + fn next_u32(&mut self) -> u32 { + impls::next_u32_via_fill(self) + } + + fn next_u64(&mut self) -> u64 { + impls::next_u64_via_fill(self) + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.try_fill_bytes(dest).unwrap_or_else(|err| + panic!("reading random bytes from Read implementation failed; error: {}", err)); + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + if dest.len() == 0 { return Ok(()); } + // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`. + self.reader.read_exact(dest).map_err(|err| { + match err.kind() { + ::std::io::ErrorKind::UnexpectedEof => Error::with_cause( + ErrorKind::Unavailable, + "not enough bytes available, reached end of source", err), + _ => Error::with_cause(ErrorKind::Unavailable, + "error reading from Read source", err) + } + }) + } +} + +#[cfg(test)] +mod test { + use super::ReadRng; + use {RngCore, ErrorKind}; + + #[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] + fn test_reader_rng_insufficient_bytes() { + let v = [1u8, 2, 3, 4, 5, 6, 7, 8]; + let mut w = [0u8; 9]; + + let mut rng = ReadRng::new(&v[..]); + + assert!(rng.try_fill_bytes(&mut w).err().unwrap().kind == ErrorKind::Unavailable); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/adapter/reseeding.rs b/crates/rand-0.5.0-pre.2/src/rngs/adapter/reseeding.rs new file mode 100644 index 0000000..7ec8de5 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/adapter/reseeding.rs @@ -0,0 +1,260 @@ +// Copyright 2013 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! A wrapper around another PRNG that reseeds it after it +//! generates a certain number of random bytes. + +use core::mem::size_of; + +use rand_core::{RngCore, CryptoRng, SeedableRng, Error, ErrorKind}; +use rand_core::block::{BlockRngCore, BlockRng}; + +/// A wrapper around any PRNG which reseeds the underlying PRNG after it has +/// generated a certain number of random bytes. +/// +/// When the RNG gets cloned, the clone is reseeded on first use. +/// +/// Reseeding is never strictly *necessary*. Cryptographic PRNGs don't have a +/// limited number of bytes they can output, or at least not a limit reachable +/// in any practical way. There is no such thing as 'running out of entropy'. +/// +/// Some small non-cryptographic PRNGs can have very small periods, for +/// example less than 2⁶⁴. Would reseeding help to ensure that you do +/// not wrap around at the end of the period? A period of 2⁶⁴ still +/// takes several centuries of CPU-years on current hardware. Reseeding will +/// actually make things worse, because the reseeded PRNG will just continue +/// somewhere else *in the same period*, with a high chance of overlapping with +/// previously used parts of it. +/// +/// # When should you use `ReseedingRng`? +/// +/// - Reseeding can be seen as some form of 'security in depth'. Even if in the +/// future a cryptographic weakness is found in the CSPRNG being used, +/// occasionally reseeding should make exploiting it much more difficult or +/// even impossible. +/// - It can be used as a poor man's cryptography (not recommended, just use a +/// good CSPRNG). Previous implementations of `thread_rng` for example used +/// `ReseedingRng` with the ISAAC RNG. That algorithm, although apparently +/// strong and with no known attack, does not come with any proof of security +/// and does not meet the current standards for a cryptographically secure +/// PRNG. By reseeding it frequently (every 32 kiB) it seems safe to assume +/// there is no attack that can operate on the tiny window between reseeds. +/// +/// # Error handling +/// +/// Although extremely unlikely, reseeding the wrapped PRNG can fail. +/// `ReseedingRng` will never panic but try to handle the error intelligently +/// through some combination of retrying and delaying reseeding until later. +/// If handling the source error fails `ReseedingRng` will continue generating +/// data from the wrapped PRNG without reseeding. +#[derive(Debug)] +pub struct ReseedingRng<R, Rsdr>(BlockRng<ReseedingCore<R, Rsdr>>) +where R: BlockRngCore + SeedableRng, + Rsdr: RngCore; + +impl<R, Rsdr> ReseedingRng<R, Rsdr> +where R: BlockRngCore + SeedableRng, + Rsdr: RngCore +{ + /// Create a new `ReseedingRng` with the given parameters. + /// + /// # Arguments + /// + /// * `rng`: the random number generator to use. + /// * `threshold`: the number of generated bytes after which to reseed the RNG. + /// * `reseeder`: the RNG to use for reseeding. + pub fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self { + ReseedingRng(BlockRng::new(ReseedingCore::new(rng, threshold, reseeder))) + } + + /// Reseed the internal PRNG. + pub fn reseed(&mut self) -> Result<(), Error> { + self.0.core.reseed() + } +} + +// TODO: this should be implemented for any type where the inner type +// implements RngCore, but we can't specify that because ReseedingCore is private +impl<R, Rsdr: RngCore> RngCore for ReseedingRng<R, Rsdr> +where R: BlockRngCore<Item = u32> + SeedableRng, + <R as BlockRngCore>::Results: AsRef<[u32]> + AsMut<[u32]> +{ + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl<R, Rsdr> Clone for ReseedingRng<R, Rsdr> +where R: BlockRngCore + SeedableRng + Clone, + Rsdr: RngCore + Clone +{ + fn clone(&self) -> ReseedingRng<R, Rsdr> { + // Recreating `BlockRng` seems easier than cloning it and resetting + // the index. + ReseedingRng(BlockRng::new(self.0.core.clone())) + } +} + +impl<R, Rsdr> CryptoRng for ReseedingRng<R, Rsdr> +where R: BlockRngCore + SeedableRng + CryptoRng, + Rsdr: RngCore + CryptoRng {} + +#[derive(Debug)] +struct ReseedingCore<R, Rsdr> { + inner: R, + reseeder: Rsdr, + threshold: i64, + bytes_until_reseed: i64, +} + +impl<R, Rsdr> BlockRngCore for ReseedingCore<R, Rsdr> +where R: BlockRngCore + SeedableRng, + Rsdr: RngCore +{ + type Item = <R as BlockRngCore>::Item; + type Results = <R as BlockRngCore>::Results; + + fn generate(&mut self, results: &mut Self::Results) { + if self.bytes_until_reseed <= 0 { + // We get better performance by not calling only `auto_reseed` here + // and continuing with the rest of the function, but by directly + // returning from a non-inlined function. + return self.reseed_and_generate(results); + } + let num_bytes = results.as_ref().len() * size_of::Self::Item(); + self.bytes_until_reseed -= num_bytes as i64; + self.inner.generate(results); + } +} + +impl<R, Rsdr> ReseedingCore<R, Rsdr> +where R: BlockRngCore + SeedableRng, + Rsdr: RngCore +{ + /// Create a new `ReseedingCore` with the given parameters. + /// + /// # Arguments + /// + /// * `rng`: the random number generator to use. + /// * `threshold`: the number of generated bytes after which to reseed the RNG. + /// * `reseeder`: the RNG to use for reseeding. + pub fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self { + assert!(threshold <= ::core::i64::MAX as u64); + ReseedingCore { + inner: rng, + reseeder, + threshold: threshold as i64, + bytes_until_reseed: threshold as i64, + } + } + + /// Reseed the internal PRNG. + fn reseed(&mut self) -> Result<(), Error> { + R::from_rng(&mut self.reseeder).map(|result| { + self.bytes_until_reseed = self.threshold; + self.inner = result + }) + } + + #[inline(never)] + fn reseed_and_generate(&mut self, + results: &mut <Self as BlockRngCore>::Results) + { + trace!("Reseeding RNG after {} generated bytes", + self.threshold - self.bytes_until_reseed); + let threshold = if let Err(e) = self.reseed() { + let delay = match e.kind { + ErrorKind::Transient => 0, + kind @ _ if kind.should_retry() => self.threshold >> 8, + _ => self.threshold, + }; + warn!("Reseeding RNG delayed reseeding by {} bytes due to \ + error from source: {}", delay, e); + delay + } else { + self.threshold + }; + + let num_bytes = results.as_ref().len() * size_of::<<R as BlockRngCore>::Item>(); + self.bytes_until_reseed = threshold - num_bytes as i64; + self.inner.generate(results); + } +} + +impl<R, Rsdr> Clone for ReseedingCore<R, Rsdr> +where R: BlockRngCore + SeedableRng + Clone, + Rsdr: RngCore + Clone +{ + fn clone(&self) -> ReseedingCore<R, Rsdr> { + ReseedingCore { + inner: self.inner.clone(), + reseeder: self.reseeder.clone(), + threshold: self.threshold, + bytes_until_reseed: 0, // reseed clone on first use + } + } +} + +impl<R, Rsdr> CryptoRng for ReseedingCore<R, Rsdr> +where R: BlockRngCore + SeedableRng + CryptoRng, + Rsdr: RngCore + CryptoRng {} + +#[cfg(test)] +mod test { + use {Rng, SeedableRng}; + use prng::chacha::ChaChaCore; + use rngs::mock::StepRng; + use super::ReseedingRng; + + #[test] + fn test_reseeding() { + let mut zero = StepRng::new(0, 0); + let rng = ChaChaCore::from_rng(&mut zero).unwrap(); + let mut reseeding = ReseedingRng::new(rng, 32*4, zero); + + // Currently we only support for arrays up to length 32. + // TODO: cannot generate seq via Rng::gen because it uses different alg + let mut buf = [0u32; 32]; // Needs to be a multiple of the RNGs result + // size to test exactly. + reseeding.fill(&mut buf); + let seq = buf; + for _ in 0..10 { + reseeding.fill(&mut buf); + assert_eq!(buf, seq); + } + } + + #[test] + fn test_clone_reseeding() { + let mut zero = StepRng::new(0, 0); + let rng = ChaChaCore::from_rng(&mut zero).unwrap(); + let mut rng1 = ReseedingRng::new(rng, 32*4, zero); + + let first: u32 = rng1.gen(); + for _ in 0..10 { let _ = rng1.gen::<u32>(); } + + let mut rng2 = rng1.clone(); + assert_eq!(first, rng2.gen::<u32>()); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/entropy.rs b/crates/rand-0.5.0-pre.2/src/rngs/entropy.rs new file mode 100644 index 0000000..e260af9 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/entropy.rs @@ -0,0 +1,177 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Entropy generator, or wrapper around external generators + +use rand_core::{RngCore, CryptoRng, Error, impls}; +use rngs::{OsRng, JitterRng}; + +/// An interface returning random data from external source(s), provided +/// specifically for securely seeding algorithmic generators (PRNGs). +/// +/// Where possible, `EntropyRng` retrieves random data from the operating +/// system's interface for random numbers ([`OsRng`]); if that fails it will +/// fall back to the [`JitterRng`] entropy collector. In the latter case it will +/// still try to use [`OsRng`] on the next usage. +/// +/// If no secure source of entropy is available `EntropyRng` will panic on use; +/// i.e. it should never output predictable data. +/// +/// This is either a little slow ([`OsRng`] requires a system call) or extremely +/// slow ([`JitterRng`] must use significant CPU time to generate sufficient +/// jitter); for better performance it is common to seed a local PRNG from +/// external entropy then primarily use the local PRNG ([`thread_rng`] is +/// provided as a convenient, local, automatically-seeded CSPRNG). +/// +/// # Panics +/// +/// On most systems, like Windows, Linux, macOS and *BSD on common hardware, it +/// is highly unlikely for both [`OsRng`] and [`JitterRng`] to fail. But on +/// combinations like webassembly without Emscripten or stdweb both sources are +/// unavailable. If both sources fail, only [`try_fill_bytes`] is able to +/// report the error, and only the one from `OsRng`. The other [`RngCore`] +/// methods will panic in case of an error. +/// +/// [`OsRng`]: struct.OsRng.html +/// [`JitterRng`]: jitter/struct.JitterRng.html +/// [`thread_rng`]: ../fn.thread_rng.html +/// [`RngCore`]: ../trait.RngCore.html +/// [`try_fill_bytes`]: ../trait.RngCore.html#method.tymethod.try_fill_bytes +#[derive(Debug)] +pub struct EntropyRng { + rng: EntropySource, +} + +#[derive(Debug)] +enum EntropySource { + Os(OsRng), + Jitter(JitterRng), + None, +} + +impl EntropyRng { + /// Create a new `EntropyRng`. + /// + /// This method will do no system calls or other initialization routines, + /// those are done on first use. This is done to make `new` infallible, + /// and `try_fill_bytes` the only place to report errors. + pub fn new() -> Self { + EntropyRng { rng: EntropySource::None } + } +} + +impl Default for EntropyRng { + fn default() -> Self { + EntropyRng::new() + } +} + +impl RngCore for EntropyRng { + fn next_u32(&mut self) -> u32 { + impls::next_u32_via_fill(self) + } + + fn next_u64(&mut self) -> u64 { + impls::next_u64_via_fill(self) + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.try_fill_bytes(dest).unwrap_or_else(|err| + panic!("all entropy sources failed; first error: {}", err)) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + fn try_os_new(dest: &mut [u8]) -> Result<OsRng, Error> + { + let mut rng = OsRng::new()?; + rng.try_fill_bytes(dest)?; + Ok(rng) + } + + fn try_jitter_new(dest: &mut [u8]) -> Result<JitterRng, Error> + { + let mut rng = JitterRng::new()?; + rng.try_fill_bytes(dest)?; + Ok(rng) + } + + let mut switch_rng = None; + match self.rng { + EntropySource::None => { + let os_rng_result = try_os_new(dest); + match os_rng_result { + Ok(os_rng) => { + debug!("EntropyRng: using OsRng"); + switch_rng = Some(EntropySource::Os(os_rng)); + } + Err(os_rng_error) => { + warn!("EntropyRng: OsRng failed [falling back to JitterRng]: {}", + os_rng_error); + match try_jitter_new(dest) { + Ok(jitter_rng) => { + debug!("EntropyRng: using JitterRng"); + switch_rng = Some(EntropySource::Jitter(jitter_rng)); + } + Err(_jitter_error) => { + warn!("EntropyRng: JitterRng failed: {}", + _jitter_error); + return Err(os_rng_error); + } + } + } + } + } + EntropySource::Os(ref mut rng) => { + let os_rng_result = rng.try_fill_bytes(dest); + if let Err(os_rng_error) = os_rng_result { + warn!("EntropyRng: OsRng failed [falling back to JitterRng]: {}", + os_rng_error); + match try_jitter_new(dest) { + Ok(jitter_rng) => { + debug!("EntropyRng: using JitterRng"); + switch_rng = Some(EntropySource::Jitter(jitter_rng)); + } + Err(_jitter_error) => { + warn!("EntropyRng: JitterRng failed: {}", + _jitter_error); + return Err(os_rng_error); + } + } + } + } + EntropySource::Jitter(ref mut rng) => { + if let Ok(os_rng) = try_os_new(dest) { + debug!("EntropyRng: using OsRng"); + switch_rng = Some(EntropySource::Os(os_rng)); + } else { + return rng.try_fill_bytes(dest); // use JitterRng + } + } + } + if let Some(rng) = switch_rng { + self.rng = rng; + } + Ok(()) + } +} + +impl CryptoRng for EntropyRng {} + +#[cfg(test)] +mod test { + use super::*; + + #[test] + fn test_entropy() { + let mut rng = EntropyRng::new(); + let n = (rng.next_u32() ^ rng.next_u32()).count_ones(); + assert!(n >= 2); // p(failure) approx 1e-7 + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/jitter.rs b/crates/rand-0.5.0-pre.2/src/rngs/jitter.rs new file mode 100644 index 0000000..a31a1df --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/jitter.rs @@ -0,0 +1,893 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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. + +// Note: the C implementation of `Jitterentropy` relies on being compiled +// without optimizations. This implementation goes through lengths to make the +// compiler not optimize out code which does influence timing jitter, but is +// technically dead code. + +use rand_core::{RngCore, CryptoRng, Error, ErrorKind, impls}; + +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 10³..10⁶ 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. +/// +/// `JitterRng` can be used without the standard library, but not conveniently, +/// you must provide a high-precision timer and carefully have to follow the +/// instructions of [`new_with_timer`]. +/// +/// This implementation is based on +/// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0. +/// +/// # Quality testing +/// +/// [`JitterRng::new()`] has build-in, but limited, quality testing, however +/// before using `JitterRng` on untested hardware, or after changes that could +/// effect how the code is optimized (such as a new LLVM version), it is +/// recommend to run the much more stringent +/// [NIST SP 800-90B Entropy Estimation Suite]( +/// https://github.com/usnistgov/SP800-90B_EntropyAssessment). +/// +/// Use the following code using [`timer_stats`] to collect the data: +/// +/// ```no_run +/// use rand::jitter::JitterRng; +/// # +/// # use std::error::Error; +/// # use std::fs::File; +/// # use std::io::Write; +/// # +/// # fn try_main() -> Result<(), Box<Error>> { +/// let mut rng = JitterRng::new()?; +/// +/// // 1_000_000 results are required for the +/// // NIST SP 800-90B Entropy Estimation Suite +/// const ROUNDS: usize = 1_000_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(); +/// # } +/// ``` +/// +/// This will produce two files: `jitter_rng_var.bin` and `jitter_rng_min.bin`. +/// Run the Entropy Estimation Suite in three configurations, as outlined below. +/// Every run has two steps. One step to produce an estimation, another to +/// validate the estimation. +/// +/// 1. Estimate the expected amount of entropy that is at least available with +/// each round of the entropy collector. This number should be greater than +/// the amount estimated with `64 / test_timer()`. +/// ```sh +/// python noniid_main.py -v jitter_rng_var.bin 8 +/// restart.py -v jitter_rng_var.bin 8 <min-entropy> +/// ``` +/// 2. Estimate 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. +/// ```sh +/// python noniid_main.py -v -u 4 jitter_rng_var.bin 4 +/// restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy> +/// ``` +/// 3. Estimate the expected amount of entropy that is available to the entropy +/// collector 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. +/// ```sh +/// python noniid_main.py -v -u 4 jitter_rng_min.bin 4 +/// restart.py -v -u 4 jitter_rng_min.bin 4 <min-entropy> +/// ``` +/// +/// [`OsRng`]: struct.OsRng.html +/// [`JitterRng::new()`]: struct.JitterRng.html#method.new +/// [`new_with_timer`]: struct.JitterRng.html#method.new_with_timer +/// [`timer_stats`]: struct.JitterRng.html#method.timer_stats +pub struct JitterRng { + data: u64, // Actual random number + // Number of rounds to run the entropy collector per 64 bits + rounds: u8, + // Timer used by `measure_jitter` + timer: fn() -> u64, + // Memory for the Memory Access noise source + mem_prev_index: u16, + // Make `next_u32` not waste 32 bits + data_half_used: bool, +} + +// Note: `JitterRng` maintains a small 64-bit entropy pool. With every +// `generate` 64 new bits should be integrated in the pool. If a round of +// `generate` were to collect less than the expected 64 bit, then the returned +// value, and the new state of the entropy pool, would be in some way related to +// the initial state. It is therefore better if the initial state of the entropy +// pool is different on each call to `generate`. This has a few implications: +// - `generate` should be called once before using `JitterRng` to produce the +// first usable value (this is done by default in `new`); +// - We do not zero the entropy pool after generating a result. The reference +// implementation also does not support zeroing, but recommends generating a +// new value without using it if you want to protect a previously generated +// 'secret' value from someone inspecting the memory; +// - Implementing `Clone` seems acceptable, as it would not cause the systematic +// bias a constant might cause. Only instead of one value that could be +// potentially related to the same initial state, there are now two. + +// Entropy collector state. +// These values are not necessary to preserve across runs. +struct EcState { + // Previous time stamp to determine the timer delta + prev_time: u64, + // Deltas used for the stuck test + last_delta: i32, + last_delta2: i32, + // Memory for the Memory Access noise source + mem: [u8; MEMORY_SIZE], +} + +impl EcState { + // 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: i32) -> 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 + } +} + +// 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 {{}}") + } +} + +impl Clone for JitterRng { + fn clone(&self) -> JitterRng { + JitterRng { + data: self.data, + rounds: self.rounds, + timer: self.timer, + mem_prev_index: self.mem_prev_index, + // The 32 bits that may still be unused from the previous round are + // for the original to use, not for the clone. + data_half_used: false, + } + } +} + +/// An error that can occur when [`JitterRng::test_timer`] fails. +/// +/// [`JitterRng::test_timer`]: struct.JitterRng.html#method.test_timer +#[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() + } +} + +impl From<TimerError> for Error { + fn from(err: TimerError) -> Error { + // Timer check is already quite permissive of failures so we don't + // expect false-positive failures, i.e. any error is irrecoverable. + Error::with_cause(ErrorKind::Unavailable, + "timer jitter failed basic quality tests", err) + } +} + +// 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, or a + /// platform-specific function with higher accuracy if necessary and + /// available. + /// + /// 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 state = JitterRng::new_with_timer(platform::get_nstime); + let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u8; + if rounds == 0 { + // No result yet: run test. + // This allows the timer test to run multiple times; we don't care. + rounds = state.test_timer()?; + JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed); + info!("JitterRng: using {} rounds per u64 output", rounds); + } + state.set_rounds(rounds); + + // Fill `data` with a non-zero value. + state.gen_entropy(); + Ok(state) + } + + /// 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`]. Also it is important to + /// consume at least one `u64` before using the first result to initialize + /// the entropy collection pool. + /// + /// # Example + /// + /// ``` + /// # use rand::{Rng, Error}; + /// use rand::jitter::JitterRng; + /// + /// # fn try_inner() -> Result<(), 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 + /// } + /// + /// let mut rng = JitterRng::new_with_timer(get_nstime); + /// let rounds = rng.test_timer()?; + /// rng.set_rounds(rounds); // optional + /// let _ = rng.gen::<u64>(); + /// + /// // Ready for use + /// let v: u64 = rng.gen(); + /// # Ok(()) + /// # } + /// + /// # let _ = try_inner(); + /// ``` + /// + /// [`test_timer`]: struct.JitterRng.html#method.test_timer + /// [`set_rounds`]: struct.JitterRng.html#method.set_rounds + pub fn new_with_timer(timer: fn() -> u64) -> JitterRng { + JitterRng { + data: 0, + rounds: 64, + timer, + mem_prev_index: 0, + data_half_used: false, + } + } + + /// 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. + /// + /// [`new_with_timer`]: struct.JitterRng.html#method.new_with_timer + pub fn set_rounds(&mut self, rounds: u8) { + 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 >>= 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 >>= 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, mem: &mut [u8; MEMORY_SIZE], 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 as usize; + 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 + mem[index] = mem[index].wrapping_add(1); + } + self.mem_prev_index = index as u16; + } + + // 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 `ec.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, ec: &mut EcState) -> Option<()> { + // Invoke one noise source before time measurement to add variations + self.memaccess(&mut ec.mem, 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(ec.prev_time) as i64 as i32; + ec.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 ec.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 { + trace!("JitterRng: collecting entropy"); + + // Prime `ec.prev_time`, and run the noice sources to make sure the + // first loop round collects the expected entropy. + let mut ec = EcState { + prev_time: (self.timer)(), + last_delta: 0, + last_delta2: 0, + mem: [0; MEMORY_SIZE], + }; + let _ = self.measure_jitter(&mut ec); + + 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(&mut ec).is_none() {} + } + + // Do a single read from `self.mem` to make sure the Memory Access noise + // source is not optimised out. + black_box(ec.mem[0]); + + 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. + /// + /// [`TimerError`]: enum.TimerError.html + #[cfg(not(all(target_arch = "wasm32", not(target_os = "emscripten"))))] + pub fn test_timer(&mut self) -> Result<u8, TimerError> { + debug!("JitterRng: testing timer ..."); + // 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. + + 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; + + let mut ec = EcState { + prev_time: (self.timer)(), + last_delta: 0, + last_delta2: 0, + mem: [0; MEMORY_SIZE], + }; + + // 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(&mut ec.mem, 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 as i32; + + // 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 ec.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; + } + + // Do a single read from `self.mem` to make sure the Memory Access noise + // source is not optimised out. + black_box(ec.mem[0]); + + // 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`. + let delta_average = delta_sum / TESTLOOPCOUNT; + + if delta_average >= 16 { + let log2 = 64 - delta_average.leading_zeros(); + // Do something similar to roundup(64/(log2/2)): + Ok( ((64u32 * 2 + log2 - 1) / log2) as u8) + } else { + // For values < 16 the rounding error becomes too large, use a + // lookup table. + // Values 0 and 1 are invalid, and filtered out by the + // `delta_sum < TESTLOOPCOUNT` test above. + let log2_lookup = [0, 0, 128, 81, 64, 56, 50, 46, + 43, 41, 39, 38, 36, 35, 34, 33]; + Ok(log2_lookup[delta_average as usize]) + } + } + #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))] + pub fn test_timer(&mut self) -> Result<u8, TimerError> { + return Err(TimerError::NoTimer); + } + + /// Statistical test: return the timer delta of one normal run of the + /// `JitterRng` 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 + /// `JitterRng` 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 the entropy collector can collect in the worst + /// case. + /// + /// See [Quality testing](struct.JitterRng.html#quality-testing) on how to + /// use `timer_stats` to test the quality of `JitterRng`. + #[cfg(feature="std")] + pub fn timer_stats(&mut self, var_rounds: bool) -> i64 { + let mut mem = [0; MEMORY_SIZE]; + + let time = platform::get_nstime(); + self.memaccess(&mut mem, 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 RngCore for JitterRng { + fn next_u32(&mut self) -> u32 { + // We want to use both parts of the generated entropy + if self.data_half_used { + self.data_half_used = false; + (self.data >> 32) as u32 + } else { + self.data = self.next_u64(); + self.data_half_used = true; + self.data as u32 + } + } + + fn next_u64(&mut self) -> u64 { + self.data_half_used = false; + self.gen_entropy() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + // Fill using `next_u32`. This is faster for filling small slices (four + // bytes or less), while the overhead is negligible. + // + // This is done especially for wrappers that implement `next_u32` + // themselves via `fill_bytes`. + impls::fill_bytes_via_next(self, dest) + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + Ok(self.fill_bytes(dest)) + } +} + +impl CryptoRng for JitterRng {} + +#[cfg(test)] +mod test_jitter_init { + use jitter::JitterRng; + + #[cfg(feature="std")] + #[test] + fn test_jitter_init() { + use RngCore; + // Because this is a debug build, measurements here are not representive + // of the final release build. + // Don't fail this test if initializing `JitterRng` fails because of a + // bad timer (the timer from the standard library may not have enough + // accuracy on all platforms). + match JitterRng::new() { + Ok(ref mut rng) => { + // false positives are possible, but extremely unlikely + assert!(rng.next_u32() | rng.next_u32() != 0); + }, + Err(_) => {}, + } + } + + #[test] + fn test_jitter_bad_timer() { + fn bad_timer() -> u64 { 0 } + let mut rng = JitterRng::new_with_timer(bad_timer); + assert!(rng.test_timer().is_err()); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/mock.rs b/crates/rand-0.5.0-pre.2/src/rngs/mock.rs new file mode 100644 index 0000000..812e4be --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/mock.rs @@ -0,0 +1,61 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Mock random number generator + +use rand_core::{RngCore, Error, impls}; + +/// A simple implementation of `RngCore` for testing purposes. +/// +/// This generates an arithmetic sequence (i.e. adds a constant each step) +/// over a `u64` number, using wrapping arithmetic. If the increment is 0 +/// the generator yields a constant. +/// +/// ``` +/// use rand::Rng; +/// use rand::rngs::mock::StepRng; +/// +/// let mut my_rng = StepRng::new(2, 1); +/// let sample: [u64; 3] = my_rng.gen(); +/// assert_eq!(sample, [2, 3, 4]); +/// ``` +#[derive(Debug, Clone)] +pub struct StepRng { + v: u64, + a: u64, +} + +impl StepRng { + /// Create a `StepRng`, yielding an arithmetic sequence starting with + /// `initial` and incremented by `increment` each time. + pub fn new(initial: u64, increment: u64) -> Self { + StepRng { v: initial, a: increment } + } +} + +impl RngCore for StepRng { + fn next_u32(&mut self) -> u32 { + self.next_u64() as u32 + } + + fn next_u64(&mut self) -> u64 { + let result = self.v; + self.v = self.v.wrapping_add(self.a); + result + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + impls::fill_bytes_via_next(self, dest); + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + Ok(self.fill_bytes(dest)) + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/mod.rs b/crates/rand-0.5.0-pre.2/src/rngs/mod.rs new file mode 100644 index 0000000..3e5c3fa --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/mod.rs @@ -0,0 +1,184 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Random number generators and adapters for common usage: +//! +//! - [`ThreadRng`], a fast, secure, auto-seeded thread-local generator +//! - [`StdRng`] and [`SmallRng`], algorithms to cover typical usage +//! - [`EntropyRng`], [`OsRng`] and [`JitterRng`] as entropy sources +//! - [`mock::StepRng`] as a simple counter for tests +//! - [`adapter::ReadRng`] to read from a file/stream +//! +//! # Background — Random number generators (RNGs) +//! +//! Computers are inherently deterministic, so to get *random* numbers one +//! either has to use a hardware generator or collect bits of *entropy* from +//! various sources (e.g. event timestamps, or jitter). This is a relatively +//! slow and complicated operation. +//! +//! Generally the operating system will collect some entropy, remove bias, and +//! use that to seed its own PRNG; [`OsRng`] provides an interface to this. +//! [`JitterRng`] is an entropy collector included with Rand that measures +//! jitter in the CPU execution time, and jitter in memory access time. +//! [`EntropyRng`] is a wrapper that uses the best entropy source that is +//! available. +//! +//! ## Pseudo-random number generators +//! +//! What is commonly used instead of "true" random number renerators, are +//! *pseudo-random number generators* (PRNGs), deterministic algorithms that +//! produce an infinite stream of pseudo-random numbers from a small random +//! seed. PRNGs are faster, and have better provable properties. The numbers +//! produced can be statistically of very high quality and can be impossible to +//! predict. (They can also have obvious correlations and be trivial to predict; +//! quality varies.) +//! +//! There are two different types of PRNGs: those developed for simulations +//! and statistics, and those developed for use in cryptography; the latter are +//! called Cryptographically Secure PRNGs (CSPRNG or CPRNG). Both types can +//! have good statistical quality but the latter also have to be impossible to +//! predict, even after seeing many previous output values. Rand provides a good +//! default algorithm from each class: +//! +//! - [`SmallRng`] is a PRNG chosen for low memory usage, high performance and +//! good statistical quality. +//! The current algorithm (plain Xorshift) unfortunately performs +//! poorly in statistical quality test suites (TestU01 and PractRand) and will +//! be replaced in the next major release. +//! - [`StdRng`] is a CSPRNG chosen for good performance and trust of security +//! (based on reviews, maturity and usage). The current algorithm is HC-128, +//! which is one of the recommendations by ECRYPT's eSTREAM project. +//! +//! The above PRNGs do not cover all use-cases; more algorithms can be found in +//! the [`prng` module], as well as in several other crates. For example, you +//! may wish a CSPRNG with significantly lower memory usage than [`StdRng`] +//! while being less concerned about performance, in which case [`ChaChaRng`] +//! is a good choice. +//! +//! One complexity is that the internal state of a PRNG must change with every +//! generated number. For APIs this generally means a mutable reference to the +//! state of the PRNG has to be passed around. +//! +//! A solution is [`ThreadRng`]. This is a thread-local implementation of +//! [`StdRng`] with automatic seeding on first use. It is the best choice if you +//! "just" want a convenient, secure, fast random number source. Use via the +//! [`thread_rng`] function, which gets a reference to the current thread's +//! local instance. +//! +//! ## Seeding +//! +//! As mentioned above, PRNGs require a random seed in order to produce random +//! output. This is especially important for CSPRNGs, which are still +//! deterministic algorithms, thus can only be secure if their seed value is +//! also secure. To seed a PRNG, use one of: +//! +//! - [`FromEntropy::from_entropy`]; this is the most convenient way to seed +//! with fresh, secure random data. +//! - [`SeedableRng::from_rng`]; this allows seeding from another PRNG or +//! from an entropy source such as [`EntropyRng`]. +//! - [`SeedableRng::from_seed`]; this is mostly useful if you wish to be able +//! to reproduce the output sequence by using a fixed seed. (Don't use +//! [`StdRng`] or [`SmallRng`] in this case since different algorithms may be +//! used by future versions of Rand; use an algorithm from the +//! [`prng` module].) +//! +//! ## Conclusion +//! +//! - [`thread_rng`] is what you often want to use. +//! - If you want more control, flexibility, or better performance, use +//! [`StdRng`], [`SmallRng`] or an algorithm from the [`prng` module]. +//! - Use [`FromEntropy::from_entropy`] to seed new PRNGs. +//! - If you need reproducibility, use [`SeedableRng::from_seed`] combined with +//! a named PRNG. +//! +//! More information and notes on cryptographic security can be found +//! in the [`prng` module]. +//! +//! ## Examples +//! +//! Examples of seeding PRNGs: +//! +//! ``` +//! use rand::prelude::*; +//! # use rand::Error; +//! +//! // StdRng seeded securely by the OS or local entropy collector: +//! let mut rng = StdRng::from_entropy(); +//! # let v: u32 = rng.gen(); +//! +//! // SmallRng seeded from thread_rng: +//! # fn try_inner() -> Result<(), Error> { +//! let mut rng = SmallRng::from_rng(thread_rng())?; +//! # let v: u32 = rng.gen(); +//! # Ok(()) +//! # } +//! # try_inner().unwrap(); +//! +//! // SmallRng seeded by a constant, for deterministic results: +//! let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16]; // byte array +//! let mut rng = SmallRng::from_seed(seed); +//! # let v: u32 = rng.gen(); +//! ``` +//! +//! +//! # Implementing custom RNGs +//! +//! If you want to implement custom RNG, see the [`rand_core`] crate. The RNG +//! will have to implement the [`RngCore`] trait, where the [`Rng`] trait is +//! build on top of. +//! +//! If the RNG needs seeding, also implement the [`SeedableRng`] trait. +//! +//! [`CryptoRng`] is a marker trait cryptographically secure PRNGs can +//! implement. +//! +//! +// This module: +//! [`ThreadRng`]: struct.ThreadRng.html +//! [`StdRng`]: struct.StdRng.html +//! [`SmallRng`]: struct.SmallRng.html +//! [`EntropyRng`]: struct.EntropyRng.html +//! [`OsRng`]: struct.OsRng.html +//! [`JitterRng`]: struct.JitterRng.html +// Other traits and functions: +//! [`rand_core`]: https://crates.io/crates/rand_core +//! [`prng` module]: ../prng/index.html +//! [`CryptoRng`]: ../trait.CryptoRng.html +//! [`FromEntropy`]: ../trait.FromEntropy.html +//! [`FromEntropy::from_entropy`]: ../trait.FromEntropy.html#tymethod.from_entropy +//! [`RngCore`]: ../trait.RngCore.html +//! [`Rng`]: ../trait.Rng.html +//! [`SeedableRng`]: ../trait.SeedableRng.html +//! [`SeedableRng::from_rng`]: ../trait.SeedableRng.html#tymethod.from_rng +//! [`SeedableRng::from_seed`]: ../trait.SeedableRng.html#tymethod.from_seed +//! [`thread_rng`]: ../fn.thread_rng.html +//! [`mock::StepRng`]: mock/struct.StepRng.html +//! [`adapter::ReadRng`]: adapter/struct.ReadRng.html +//! [`ChaChaRng`]: ../prng/chacha/struct.ChaChaRng.html + +pub mod adapter; + +#[cfg(feature="std")] mod entropy; +#[doc(hidden)] pub mod jitter; +pub mod mock; // Public so we don't export `StepRng` directly, making it a bit + // more clear it is intended for testing. +#[cfg(feature="std")] #[doc(hidden)] pub mod os; +mod small; +mod std; +#[cfg(feature="std")] pub(crate) mod thread; + + +pub use self::jitter::{JitterRng, TimerError}; +#[cfg(feature="std")] pub use self::entropy::EntropyRng; +#[cfg(feature="std")] pub use self::os::OsRng; + +pub use self::small::SmallRng; +pub use self::std::StdRng; +#[cfg(feature="std")] pub use self::thread::ThreadRng; diff --git a/crates/rand-0.5.0-pre.2/src/rngs/os.rs b/crates/rand-0.5.0-pre.2/src/rngs/os.rs new file mode 100644 index 0000000..2239d45 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/os.rs @@ -0,0 +1,852 @@ +// Copyright 2013-2015 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Interface to the random number generator of the operating system. + +use std::fmt; +use rand_core::{CryptoRng, RngCore, Error, impls}; + +/// A random number generator that retrieves randomness straight from the +/// operating system. +/// +/// This is the preferred external source of entropy for most applications. +/// Commonly it is used to initialize a user-space RNG, which can then be used +/// to generate random values with much less overhead than `OsRng`. +/// +/// You may prefer to use [`EntropyRng`] instead of `OsRng`. It is unlikely, but +/// not entirely theoretical, for `OsRng` to fail. In such cases [`EntropyRng`] +/// falls back on a good alternative entropy source. +/// +/// `OsRng` usually does not block. On some systems, and notably virtual +/// machines, it may block very early in the init process, when the OS CSPRNG +/// has not yet been seeded. +/// +/// `OsRng::new()` is guaranteed to be very cheap (after the first successful +/// call), and will never consume more than one file handle per process. +/// +/// # Platform sources +/// +/// - Linux, Android: reads from the `getrandom(2)` system call if available, +/// otherwise from `/dev/urandom`. +/// - macOS, iOS: calls `SecRandomCopyBytes`. +/// - Windows: calls `RtlGenRandom`. +/// - WASM (with `stdweb` feature): calls `window.crypto.getRandomValues` in +/// browsers, and in Node.js `require("crypto").randomBytes`. +/// - Emscripten: reads from emulated `/dev/urandom`, which maps to the same +/// interfaces as `stdweb`, but falls back to the insecure `Math.random()` if +/// unavailable. +/// - OpenBSD: calls `getentropy(2)`. +/// - FreeBSD: uses the `kern.arandom` `sysctl(2)` mib. +/// - Fuchsia: calls `cprng_draw`. +/// - Redox: reads from `rand:` device. +/// - CloudABI: calls `random_get`. +/// - Other Unix-like systems: reads directly from `/dev/urandom`. +/// +/// ## Notes on Unix `/dev/urandom` +/// +/// Many Unix systems provide `/dev/random` as well as `/dev/urandom`. On all +/// modern systems these two interfaces offer identical quality, with the +/// difference that on some systems `/dev/random` may block. This is a dated +/// design, and `/dev/urandom` is preferred by cryptography experts. +/// See [Myths about urandom](https://www.2uo.de/myths-about-urandom/). +/// +/// On some systems reading from `/dev/urandom` “may return data prior to the +/// entropy pool being initialized”. I.e., early in the boot process, and +/// especially on virtual machines, `/dev/urandom` may return data that is less +/// random. As a countermeasure we try to do a single read from `/dev/random` in +/// non-blocking mode. If the OS RNG is not yet properly seeded, we will get an +/// error. Because we keep one file descriptor to `/dev/urandom` open when +/// succesful, this is only a small one-time cost. +/// +/// # Panics +/// +/// `OsRng` is extremely unlikely to fail if `OsRng::new()` was succesfull. But +/// in case it does fail, only [`try_fill_bytes`] is able to report the cause. +/// Depending on the error the other [`RngCore`] methods will retry several +/// times, and panic in case the error remains. +/// +/// [`EntropyRng`]: struct.EntropyRng.html +/// [`RngCore`]: ../trait.RngCore.html +/// [`try_fill_bytes`]: ../trait.RngCore.html#method.tymethod.try_fill_bytes + + +#[allow(unused)] // not used by all targets +#[derive(Clone)] +pub struct OsRng(imp::OsRng); + +impl fmt::Debug for OsRng { + fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { + self.0.fmt(f) + } +} + +impl OsRng { + /// Create a new `OsRng`. + pub fn new() -> Result<OsRng, Error> { + imp::OsRng::new().map(OsRng) + } +} + +impl CryptoRng for OsRng {} + +impl RngCore for OsRng { + fn next_u32(&mut self) -> u32 { + impls::next_u32_via_fill(self) + } + + fn next_u64(&mut self) -> u64 { + impls::next_u64_via_fill(self) + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + use std::{time, thread}; + + // We cannot return Err(..), so we try to handle before panicking. + const MAX_RETRY_PERIOD: u32 = 10; // max 10s + const WAIT_DUR_MS: u32 = 100; // retry every 100ms + let wait_dur = time::Duration::from_millis(WAIT_DUR_MS as u64); + const RETRY_LIMIT: u32 = (MAX_RETRY_PERIOD * 1000) / WAIT_DUR_MS; + const TRANSIENT_RETRIES: u32 = 8; + let mut err_count = 0; + let mut error_logged = false; + + loop { + if let Err(e) = self.try_fill_bytes(dest) { + if err_count >= RETRY_LIMIT { + error!("OsRng failed too many times; last error: {}", e); + panic!("OsRng failed too many times; last error: {}", e); + } + + if e.kind.should_wait() { + if !error_logged { + warn!("OsRng failed; waiting up to {}s and retrying. Error: {}", + MAX_RETRY_PERIOD, e); + error_logged = true; + } + err_count += 1; + thread::sleep(wait_dur); + continue; + } else if e.kind.should_retry() { + if !error_logged { + warn!("OsRng failed; retrying up to {} times. Error: {}", + TRANSIENT_RETRIES, e); + error_logged = true; + } + err_count += (RETRY_LIMIT + TRANSIENT_RETRIES - 1) + / TRANSIENT_RETRIES; // round up + continue; + } else { + error!("OsRng failed: {}", e); + panic!("OsRng fatal error: {}", e); + } + } + + break; + } + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +#[cfg(all(unix, + not(target_os = "cloudabi"), + not(target_os = "freebsd"), + not(target_os = "fuchsia"), + not(target_os = "ios"), + not(target_os = "macos"), + not(target_os = "openbsd"), + not(target_os = "redox")))] +mod imp { + extern crate libc; + use {Error, ErrorKind}; + use std::fs::{OpenOptions, File}; + use std::os::unix::fs::OpenOptionsExt; + use std::io; + use std::io::Read; + use std::sync::{Once, Mutex, ONCE_INIT}; + + #[derive(Clone, Debug)] + pub struct OsRng(OsRngMethod); + + #[derive(Clone, Debug)] + enum OsRngMethod { + GetRandom, + RandomDevice, + } + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + if is_getrandom_available() { + return Ok(OsRng(OsRngMethod::GetRandom)); + } + + open_dev_random()?; + Ok(OsRng(OsRngMethod::RandomDevice)) + } + + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + match self.0 { + OsRngMethod::GetRandom => getrandom_try_fill(dest), + OsRngMethod::RandomDevice => dev_random_try_fill(dest), + } + } + } + + #[cfg(all(any(target_os = "linux", target_os = "android"), + any(target_arch = "x86_64", target_arch = "x86", + target_arch = "arm", target_arch = "aarch64", + target_arch = "s390x", target_arch = "powerpc", + target_arch = "mips", target_arch = "mips64")))] + 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 = "s390x")] + const NR_GETRANDOM: libc::c_long = 349; + #[cfg(target_arch = "powerpc")] + const NR_GETRANDOM: libc::c_long = 359; + #[cfg(target_arch = "mips")] // old ABI + const NR_GETRANDOM: libc::c_long = 4353; + #[cfg(target_arch = "mips64")] + const NR_GETRANDOM: libc::c_long = 5313; + + const GRND_NONBLOCK: libc::c_uint = 0x0001; + + unsafe { + syscall(NR_GETRANDOM, buf.as_mut_ptr(), buf.len(), GRND_NONBLOCK) + } + } + + #[cfg(not(all(any(target_os = "linux", target_os = "android"), + any(target_arch = "x86_64", target_arch = "x86", + target_arch = "arm", target_arch = "aarch64", + target_arch = "s390x", target_arch = "powerpc", + target_arch = "mips", target_arch = "mips64"))))] + fn getrandom(_buf: &mut [u8]) -> libc::c_long { -1 } + + fn getrandom_try_fill(dest: &mut [u8]) -> Result<(), Error> { + trace!("OsRng: reading {} bytes via getrandom", dest.len()); + let mut read = 0; + let len = dest.len(); + while read < len { + let result = getrandom(&mut dest[read..]); + if result == -1 { + let err = io::Error::last_os_error(); + let kind = err.kind(); + if kind == io::ErrorKind::Interrupted { + continue; + } else if kind == io::ErrorKind::WouldBlock { + // Potentially this would waste bytes, but since we use + // /dev/urandom blocking only happens if not initialised. + // Also, wasting the bytes in dest doesn't matter very much. + return Err(Error::with_cause( + ErrorKind::NotReady, + "getrandom not ready", + err, + )); + } else { + return Err(Error::with_cause( + ErrorKind::Unavailable, + "unexpected getrandom error", + err, + )); + } + } else { + read += result as usize; + } + } + Ok(()) + } + + #[cfg(all(any(target_os = "linux", target_os = "android"), + any(target_arch = "x86_64", target_arch = "x86", + target_arch = "arm", target_arch = "aarch64", + target_arch = "s390x", target_arch = "powerpc", + target_arch = "mips", target_arch = "mips64")))] + 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(|| { + debug!("OsRng: testing getrandom"); + 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); + info!("OsRng: using {}", if available { "getrandom" } else { "/dev/urandom" }); + }); + + AVAILABLE.load(Ordering::Relaxed) + } + + #[cfg(not(all(any(target_os = "linux", target_os = "android"), + any(target_arch = "x86_64", target_arch = "x86", + target_arch = "arm", target_arch = "aarch64", + target_arch = "s390x", target_arch = "powerpc", + target_arch = "mips", target_arch = "mips64"))))] + fn is_getrandom_available() -> bool { false } + + // TODO: remove outer Option when `Mutex::new(None)` is a constant expression + static mut READ_RNG_FILE: Option<Mutex<Option<File>>> = None; + static READ_RNG_ONCE: Once = ONCE_INIT; + + // Note: all instances use a single internal file handle, to prevent + // possible exhaustion of file descriptors. + // + // We do a single read from `/dev/random` in non-blocking mode. If the OS + // RNG is not yet properly seeded, we will get an error, instead of silently + // getting less random bytes, as `/dev/urandom` can return. Because we keep + // `/dev/urandom` open when succesful, this is only a small one-time cost. + fn open_dev_random() -> Result<(), Error> { + fn map_err(err: io::Error) -> Error { + match err.kind() { + io::ErrorKind::Interrupted => + Error::new(ErrorKind::Transient, "interrupted"), + io::ErrorKind::WouldBlock => + Error::with_cause(ErrorKind::NotReady, + "OS RNG not yet seeded", err), + _ => Error::with_cause(ErrorKind::Unavailable, + "error while opening random device", err) + } + } + + READ_RNG_ONCE.call_once(|| { + unsafe { READ_RNG_FILE = Some(Mutex::new(None)) } + }); + + // We try opening the file outside the `call_once` fn because we cannot + // clone the error, thus we must retry on failure. + + let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() }; + let mut guard = mutex.lock().unwrap(); + if (*guard).is_none() { + { + info!("OsRng: opening random device /dev/random"); + let mut file = OpenOptions::new() + .read(true) + .custom_flags(libc::O_NONBLOCK) + .open("/dev/random") + .map_err(map_err)?; + let mut buf = [0u8; 1]; + file.read_exact(&mut buf).map_err(map_err)?; + } + + info!("OsRng: opening random device /dev/urandom"); + let file = File::open("/dev/urandom").map_err(map_err)?; + *guard = Some(file); + }; + Ok(()) + } + + fn dev_random_try_fill(dest: &mut [u8]) -> Result<(), Error> { + if dest.len() == 0 { return Ok(()); } + trace!("OsRng: reading {} bytes from random device", dest.len()); + + // We expect this function only to be used after `open_dev_random` was + // succesful. Therefore we can assume that our memory was set with a + // valid object. + let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() }; + let mut guard = mutex.lock().unwrap(); + let file = (*guard).as_mut().unwrap(); + // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`. + file.read_exact(dest).map_err(|err| { + match err.kind() { + ::std::io::ErrorKind::WouldBlock => Error::with_cause( + ErrorKind::NotReady, + "reading from random device would block", err), + _ => Error::with_cause(ErrorKind::Unavailable, + "error reading random device", err) + } + }) + } +} + +#[cfg(target_os = "cloudabi")] +mod imp { + extern crate cloudabi; + + use {Error, ErrorKind}; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Ok(OsRng) + } + + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + trace!("OsRng: reading {} bytes via cloadabi::random_get", dest.len()); + let errno = unsafe { cloudabi::random_get(dest) }; + if errno == cloudabi::errno::SUCCESS { + Ok(()) + } else { + // Cloudlibc provides its own `strerror` implementation so we + // can use `from_raw_os_error` here. + Err(Error::with_cause( + ErrorKind::Unavailable, + "random_get() system call failed", + io::Error::from_raw_os_error(errno), + )) + } + } + } +} + +#[cfg(any(target_os = "macos", target_os = "ios"))] +mod imp { + extern crate libc; + + use {Error, ErrorKind}; + + use std::io; + use self::libc::{c_int, size_t}; + + #[derive(Clone, 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() -> Result<OsRng, Error> { + Ok(OsRng) + } + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + trace!("OsRng: reading {} bytes via SecRandomCopyBytes", dest.len()); + let ret = unsafe { + SecRandomCopyBytes(kSecRandomDefault, dest.len() as size_t, dest.as_mut_ptr()) + }; + if ret == -1 { + Err(Error::with_cause( + ErrorKind::Unavailable, + "couldn't generate random bytes", + io::Error::last_os_error())) + } else { + Ok(()) + } + } + } +} + +#[cfg(target_os = "freebsd")] +mod imp { + extern crate libc; + + use {Error, ErrorKind}; + + use std::ptr; + use std::io; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Ok(OsRng) + } + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + let mib = [libc::CTL_KERN, libc::KERN_ARND]; + trace!("OsRng: reading {} bytes via kern.arandom", dest.len()); + // kern.arandom permits a maximum buffer size of 256 bytes + for s in dest.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() { + return Err(Error::with_cause( + ErrorKind::Unavailable, + "kern.arandom sysctl failed", + io::Error::last_os_error())); + } + } + Ok(()) + } + } +} + +#[cfg(target_os = "openbsd")] +mod imp { + extern crate libc; + + use {Error, ErrorKind}; + + use std::io; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Ok(OsRng) + } + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + // getentropy(2) permits a maximum buffer size of 256 bytes + for s in dest.chunks_mut(256) { + trace!("OsRng: reading {} bytes via getentropy", s.len()); + let ret = unsafe { + libc::getentropy(s.as_mut_ptr() as *mut libc::c_void, s.len()) + }; + if ret == -1 { + return Err(Error::with_cause( + ErrorKind::Unavailable, + "getentropy failed", + io::Error::last_os_error())); + } + } + Ok(()) + } + } +} + +#[cfg(target_os = "redox")] +mod imp { + use {Error, ErrorKind}; + use std::fs::File; + use std::io::Read; + use std::io::ErrorKind::*; + use std::sync::{Once, Mutex, ONCE_INIT}; + + #[derive(Clone, Debug)] + pub struct OsRng(); + + // TODO: remove outer Option when `Mutex::new(None)` is a constant expression + static mut READ_RNG_FILE: Option<Mutex<Option<File>>> = None; + static READ_RNG_ONCE: Once = ONCE_INIT; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + READ_RNG_ONCE.call_once(|| { + unsafe { READ_RNG_FILE = Some(Mutex::new(None)) } + }); + + // We try opening the file outside the `call_once` fn because we cannot + // clone the error, thus we must retry on failure. + + let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() }; + let mut guard = mutex.lock().unwrap(); + if (*guard).is_none() { + info!("OsRng: opening random device 'rand:'"); + let file = File::open("rand:").map_err(|err| { + match err.kind() { + Interrupted => Error::new(ErrorKind::Transient, "interrupted"), + WouldBlock => Error::with_cause(ErrorKind::NotReady, + "opening random device would block", err), + _ => Error::with_cause(ErrorKind::Unavailable, + "error while opening random device", err) + } + })?; + *guard = Some(file); + }; + Ok(OsRng()) + } + + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + if dest.len() == 0 { return Ok(()); } + trace!("OsRng: reading {} bytes from random device", dest.len()); + + // Since we have an instance of Self, we can assume that our memory was + // set with a valid object. + let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() }; + let mut guard = mutex.lock().unwrap(); + let file = (*guard).as_mut().unwrap(); + // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`. + file.read_exact(dest).map_err(|err| { + Error::with_cause(ErrorKind::Unavailable, + "error reading random device", err) + }) + } + } +} + +#[cfg(target_os = "fuchsia")] +mod imp { + extern crate fuchsia_zircon; + + use {Error, ErrorKind}; + + use std::io; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Ok(OsRng) + } + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + for s in dest.chunks_mut(fuchsia_zircon::sys::ZX_CPRNG_DRAW_MAX_LEN) { + trace!("OsRng: reading {} bytes via cprng_draw", s.len()); + let mut filled = 0; + while filled < s.len() { + match fuchsia_zircon::cprng_draw(&mut s[filled..]) { + Ok(actual) => filled += actual, + Err(e) => { + return Err(Error::with_cause( + ErrorKind::Unavailable, + "cprng_draw failed", + e)); + } + }; + } + } + Ok(()) + } + } +} + +#[cfg(windows)] +mod imp { + extern crate winapi; + + use {Error, ErrorKind}; + + use std::io; + + use self::winapi::shared::minwindef::ULONG; + use self::winapi::um::ntsecapi::RtlGenRandom; + use self::winapi::um::winnt::PVOID; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Ok(OsRng) + } + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + // RtlGenRandom takes an ULONG (u32) for the length so we need to + // split up the buffer. + for slice in dest.chunks_mut(<ULONG>::max_value() as usize) { + trace!("OsRng: reading {} bytes via RtlGenRandom", slice.len()); + let ret = unsafe { + RtlGenRandom(slice.as_mut_ptr() as PVOID, slice.len() as ULONG) + }; + if ret == 0 { + return Err(Error::with_cause( + ErrorKind::Unavailable, + "couldn't generate random bytes", + io::Error::last_os_error())); + } + } + Ok(()) + } + } +} + +#[cfg(all(target_arch = "wasm32", + not(target_os = "emscripten"), + not(feature = "stdweb")))] +mod imp { + use {Error, ErrorKind}; + + #[derive(Clone, Debug)] + pub struct OsRng; + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + Err(Error::new(ErrorKind::Unavailable, + "not supported on WASM without stdweb")) + } + + pub fn try_fill_bytes(&mut self, _v: &mut [u8]) -> Result<(), Error> { + Err(Error::new(ErrorKind::Unavailable, + "not supported on WASM without stdweb")) + } + } +} + +#[cfg(all(target_arch = "wasm32", + not(target_os = "emscripten"), + feature = "stdweb"))] +mod imp { + use std::mem; + use stdweb::unstable::TryInto; + use stdweb::web::error::Error as WebError; + use {Error, ErrorKind}; + + #[derive(Clone, Debug)] + enum OsRngInner { + Browser, + Node + } + + #[derive(Clone, Debug)] + pub struct OsRng(OsRngInner); + + impl OsRng { + pub fn new() -> Result<OsRng, Error> { + let result = js! { + try { + if ( + typeof window === "object" && + typeof window.crypto === "object" && + typeof window.crypto.getRandomValues === "function" + ) { + return { success: true, ty: 1 }; + } + + if (typeof require("crypto").randomBytes === "function") { + return { success: true, ty: 2 }; + } + + return { success: false, error: new Error("not supported") }; + } catch(err) { + return { success: false, error: err }; + } + }; + + if js!{ return @{ result.as_ref() }.success } == true { + let ty = js!{ return @{ result }.ty }; + + if ty == 1 { Ok(OsRng(OsRngInner::Browser)) } + else if ty == 2 { Ok(OsRng(OsRngInner::Node)) } + else { unreachable!() } + } else { + let err: WebError = js!{ return @{ result }.error }.try_into().unwrap(); + Err(Error::with_cause(ErrorKind::Unavailable, "WASM Error", err)) + } + } + + pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + assert_eq!(mem::size_of::<usize>(), 4); + + let len = dest.len() as u32; + let ptr = dest.as_mut_ptr() as i32; + + let result = match self.0 { + OsRngInner::Browser => js! { + try { + let array = new Uint8Array(@{ len }); + window.crypto.getRandomValues(array); + HEAPU8.set(array, @{ ptr }); + + return { success: true }; + } catch(err) { + return { success: false, error: err }; + } + }, + OsRngInner::Node => js! { + try { + let bytes = require("crypto").randomBytes(@{ len }); + HEAPU8.set(new Uint8Array(bytes), @{ ptr }); + + return { success: true }; + } catch(err) { + return { success: false, error: err }; + } + } + }; + + if js!{ return @{ result.as_ref() }.success } == true { + Ok(()) + } else { + let err: WebError = js!{ return @{ result }.error }.try_into().unwrap(); + Err(Error::with_cause(ErrorKind::Unexpected, "WASM Error", err)) + } + } + } +} + +#[cfg(test)] +mod test { + use RngCore; + use OsRng; + + #[test] + fn test_os_rng() { + let mut r = OsRng::new().unwrap(); + + r.next_u32(); + r.next_u64(); + + let mut v1 = [0u8; 1000]; + r.fill_bytes(&mut v1); + + let mut v2 = [0u8; 1000]; + r.fill_bytes(&mut v2); + + let mut n_diff_bits = 0; + for i in 0..v1.len() { + n_diff_bits += (v1[i] ^ v2[i]).count_ones(); + } + + // Check at least 1 bit per byte differs. p(failure) < 1e-1000 with random input. + assert!(n_diff_bits >= v1.len() as u32); + } + + #[cfg(not(any(target_arch = "wasm32", target_arch = "asmjs")))] + #[test] + fn test_os_rng_tasks() { + use std::sync::mpsc::channel; + use std::thread; + + 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/crates/rand-0.5.0-pre.2/src/rngs/small.rs b/crates/rand-0.5.0-pre.2/src/rngs/small.rs new file mode 100644 index 0000000..effdbff --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/small.rs @@ -0,0 +1,101 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! A small fast RNG + +use {RngCore, SeedableRng, Error}; +use prng::XorShiftRng; + +/// An RNG recommended when small state, cheap initialization and good +/// performance are required. The PRNG algorithm in `SmallRng` is chosen to be +/// efficient on the current platform, **without consideration for cryptography +/// or security**. The size of its state is much smaller than for [`StdRng`]. +/// +/// Reproducibility of output from this generator is however not required, thus +/// future library versions may use a different internal generator with +/// different output. Further, this generator may not be portable and can +/// produce different output depending on the architecture. If you require +/// reproducible output, use a named RNG, for example [`XorShiftRng`]. +/// +/// The current algorithm used on all platforms is [Xorshift]. +/// +/// # Examples +/// +/// Initializing `SmallRng` with a random seed can be done using [`FromEntropy`]: +/// +/// ``` +/// # use rand::Rng; +/// use rand::FromEntropy; +/// use rand::rngs::SmallRng; +/// +/// // Create small, cheap to initialize and fast RNG with a random seed. +/// // The randomness is supplied by the operating system. +/// let mut small_rng = SmallRng::from_entropy(); +/// # let v: u32 = small_rng.gen(); +/// ``` +/// +/// When initializing a lot of `SmallRng`'s, using [`thread_rng`] can be more +/// efficient: +/// +/// ``` +/// use std::iter; +/// use rand::{SeedableRng, thread_rng}; +/// use rand::rngs::SmallRng; +/// +/// // Create a big, expensive to initialize and slower, but unpredictable RNG. +/// // This is cached and done only once per thread. +/// let mut thread_rng = thread_rng(); +/// // Create small, cheap to initialize and fast RNGs with random seeds. +/// // One can generally assume this won't fail. +/// let rngs: Vec<SmallRng> = iter::repeat(()) +/// .map(|()| SmallRng::from_rng(&mut thread_rng).unwrap()) +/// .take(10) +/// .collect(); +/// ``` +/// +/// [`FromEntropy`]: ../trait.FromEntropy.html +/// [`StdRng`]: struct.StdRng.html +/// [`thread_rng`]: ../fn.thread_rng.html +/// [Xorshift]: ../prng/struct.XorShiftRng.html +/// [`XorShiftRng`]: ../prng/struct.XorShiftRng.html +#[derive(Clone, Debug)] +pub struct SmallRng(XorShiftRng); + +impl RngCore for SmallRng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest); + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for SmallRng { + type Seed = <XorShiftRng as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + SmallRng(XorShiftRng::from_seed(seed)) + } + + fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { + XorShiftRng::from_rng(rng).map(SmallRng) + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/std.rs b/crates/rand-0.5.0-pre.2/src/rngs/std.rs new file mode 100644 index 0000000..1451f76 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/std.rs @@ -0,0 +1,83 @@ +// Copyright 2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! The standard RNG + +use {RngCore, CryptoRng, Error, SeedableRng}; +use prng::Hc128Rng; + +/// The standard RNG. The PRNG algorithm in `StdRng` is chosen to be efficient +/// on the current platform, to be statistically strong and unpredictable +/// (meaning a cryptographically secure PRNG). +/// +/// The current algorithm used on all platforms is [HC-128]. +/// +/// Reproducibility of output from this generator is however not required, thus +/// future library versions may use a different internal generator with +/// different output. Further, this generator may not be portable and can +/// produce different output depending on the architecture. If you require +/// reproducible output, use a named RNG, for example [`ChaChaRng`]. +/// +/// [HC-128]: ../prng/hc128/struct.Hc128Rng.html +/// [`ChaChaRng`]: ../prng/chacha/struct.ChaChaRng.html +#[derive(Clone, Debug)] +pub struct StdRng(Hc128Rng); + +impl RngCore for StdRng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + self.0.next_u32() + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + self.0.next_u64() + } + + fn fill_bytes(&mut self, dest: &mut [u8]) { + self.0.fill_bytes(dest); + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + self.0.try_fill_bytes(dest) + } +} + +impl SeedableRng for StdRng { + type Seed = <Hc128Rng as SeedableRng>::Seed; + + fn from_seed(seed: Self::Seed) -> Self { + StdRng(Hc128Rng::from_seed(seed)) + } + + fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> { + Hc128Rng::from_rng(rng).map(StdRng) + } +} + +impl CryptoRng for StdRng {} + + +#[cfg(test)] +mod test { + use {RngCore, SeedableRng}; + use rngs::StdRng; + + #[test] + fn test_stdrng_construction() { + let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0, + 0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; + let mut rng1 = StdRng::from_seed(seed); + assert_eq!(rng1.next_u64(), 15759097995037006553); + + let mut rng2 = StdRng::from_rng(rng1).unwrap(); + assert_eq!(rng2.next_u64(), 6766915756997287454); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/rngs/thread.rs b/crates/rand-0.5.0-pre.2/src/rngs/thread.rs new file mode 100644 index 0000000..391a358 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/rngs/thread.rs @@ -0,0 +1,141 @@ +// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +//! Thread-local random number generator + +use std::cell::UnsafeCell; +use std::rc::Rc; + +use {RngCore, CryptoRng, SeedableRng, Error}; +use rngs::adapter::ReseedingRng; +use rngs::EntropyRng; +use prng::hc128::Hc128Core; + +// Rationale for using `UnsafeCell` in `ThreadRng`: +// +// Previously we used a `RefCell`, with an overhead of ~15%. There will only +// ever be one mutable reference to the interior of the `UnsafeCell`, because +// we only have such a reference inside `next_u32`, `next_u64`, etc. Within a +// single thread (which is the definition of `ThreadRng`), there will only ever +// be one of these methods active at a time. +// +// A possible scenario where there could be multiple mutable references is if +// `ThreadRng` is used inside `next_u32` and co. But the implementation is +// completely under our control. We just have to ensure none of them use +// `ThreadRng` internally, which is nonsensical anyway. We should also never run +// `ThreadRng` in destructors of its implementation, which is also nonsensical. +// +// The additional `Rc` is not strictly neccesary, and could be removed. For now +// it ensures `ThreadRng` stays `!Send` and `!Sync`, and implements `Clone`. + + +// Number of generated bytes after which to reseed `TreadRng`. +// +// The time it takes to reseed HC-128 is roughly equivalent to generating 7 KiB. +// We pick a treshold here that is large enough to not reduce the average +// performance too much, but also small enough to not make reseeding something +// that basically never happens. +const THREAD_RNG_RESEED_THRESHOLD: u64 = 32*1024*1024; // 32 MiB + +/// The type returned by [`thread_rng`], essentially just a reference to the +/// PRNG in thread-local memory. +/// +/// `ThreadRng` uses [`ReseedingRng`] wrapping the same PRNG as [`StdRng`], +/// which is reseeded after generating 32 MiB of random data. A single instance +/// is cached per thread and the returned `ThreadRng` is a reference to this +/// instance — hence `ThreadRng` is neither `Send` nor `Sync` but is safe to use +/// within a single thread. This RNG is seeded and reseeded via [`EntropyRng`] +/// as required. +/// +/// Note that the reseeding is done as an extra precaution against entropy +/// leaks and is in theory unnecessary — to predict `ThreadRng`'s output, an +/// attacker would have to either determine most of the RNG's seed or internal +/// state, or crack the algorithm used. +/// +/// Like [`StdRng`], `ThreadRng` is a cryptographically secure PRNG. The current +/// algorithm used is [HC-128], which is an array-based PRNG that trades memory +/// usage for better performance. This makes it similar to ISAAC, the algorithm +/// used in `ThreadRng` before rand 0.5. +/// +/// Cloning this handle just produces a new reference to the same thread-local +/// generator. +/// +/// [`thread_rng`]: ../fn.thread_rng.html +/// [`ReseedingRng`]: adapter/struct.ReseedingRng.html +/// [`StdRng`]: struct.StdRng.html +/// [`EntropyRng`]: struct.EntropyRng.html +/// [HC-128]: ../prng/hc128/struct.Hc128Rng.html +#[derive(Clone, Debug)] +pub struct ThreadRng { + rng: Rc<UnsafeCell<ReseedingRng<Hc128Core, EntropyRng>>>, +} + +thread_local!( + static THREAD_RNG_KEY: Rc<UnsafeCell<ReseedingRng<Hc128Core, EntropyRng>>> = { + let mut entropy_source = EntropyRng::new(); + let r = Hc128Core::from_rng(&mut entropy_source).unwrap_or_else(|err| + panic!("could not initialize thread_rng: {}", err)); + let rng = ReseedingRng::new(r, + THREAD_RNG_RESEED_THRESHOLD, + entropy_source); + Rc::new(UnsafeCell::new(rng)) + } +); + +/// 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>()`, or cached locally, e.g. +/// `let mut rng = thread_rng();`. +/// +/// For more information see [`ThreadRng`]. +/// +/// [`ThreadRng`]: rngs/struct.ThreadRng.html +pub fn thread_rng() -> ThreadRng { + ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) } +} + +impl RngCore for ThreadRng { + #[inline(always)] + fn next_u32(&mut self) -> u32 { + unsafe { (*self.rng.get()).next_u32() } + } + + #[inline(always)] + fn next_u64(&mut self) -> u64 { + unsafe { (*self.rng.get()).next_u64() } + } + + fn fill_bytes(&mut self, bytes: &mut [u8]) { + unsafe { (*self.rng.get()).fill_bytes(bytes) } + } + + fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { + unsafe { (*self.rng.get()).try_fill_bytes(dest) } + } +} + +impl CryptoRng for ThreadRng {} + + +#[cfg(test)] +mod test { + #[test] + #[cfg(not(feature="stdweb"))] + fn test_thread_rng() { + use 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); + } +} diff --git a/crates/rand-0.5.0-pre.2/src/seq.rs b/crates/rand-0.5.0-pre.2/src/seq.rs new file mode 100644 index 0000000..68f7ab0 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/src/seq.rs @@ -0,0 +1,335 @@ +// Copyright 2017 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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 +/// +/// ``` +/// 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 + ?Sized, +{ + 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 +/// +/// ``` +/// 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 + ?Sized, + 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 +/// +/// ``` +/// 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 + ?Sized +{ + 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 + ?Sized, +{ + 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 + ?Sized, +{ + 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); + indices.swap(i, j); + } + 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 + ?Sized, +{ + 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 {XorShiftRng, Rng, SeedableRng}; + #[cfg(not(feature="std"))] + use alloc::Vec; + + #[test] + fn test_sample_iter() { + let min_val = 1; + let max_val = 100; + + let mut r = ::test::rng(401); + 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 = ::test::rng(402); + + // sample 0 items + assert_eq!(&sample_slice(&mut r, empty, 0)[..], [0u8; 0]); + assert_eq!(&sample_slice(&mut r, &[42, 2, 42], 0)[..], [0u8; 0]); + + // sample 1 item + assert_eq!(&sample_slice(&mut r, &[42], 1)[..], [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[..] == [42, 133] || v[..] == [133, 42]); + + assert_eq!(&sample_indices_inplace(&mut r, 0, 0)[..], [0usize; 0]); + assert_eq!(&sample_indices_inplace(&mut r, 1, 0)[..], [0usize; 0]); + assert_eq!(&sample_indices_inplace(&mut r, 1, 1)[..], [0]); + + assert_eq!(&sample_indices_cache(&mut r, 0, 0)[..], [0usize; 0]); + assert_eq!(&sample_indices_cache(&mut r, 1, 0)[..], [0usize; 0]); + assert_eq!(&sample_indices_cache(&mut r, 1, 1)[..], [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 = ::test::rng(403); + + for length in 1usize..max_range { + let amount = r.gen_range(0, length); + let mut seed = [0u8; 16]; + r.fill(&mut 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/crates/rand-0.5.0-pre.2/tests/bool.rs b/crates/rand-0.5.0-pre.2/tests/bool.rs new file mode 100644 index 0000000..c4208a0 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/tests/bool.rs @@ -0,0 +1,23 @@ +#![no_std] + +extern crate rand; + +use rand::SeedableRng; +use rand::rngs::SmallRng; +use rand::distributions::{Distribution, Bernoulli}; + +/// This test should make sure that we don't accidentally have undefined +/// behavior for large propabilties due to +/// https://github.com/rust-lang/rust/issues/10184. +/// Expressions like `1.0*(u64::MAX as f64) as u64` have to be avoided. +#[test] +fn large_probability() { + let p = 1. - ::core::f64::EPSILON / 2.; + assert!(p < 1.); + let d = Bernoulli::new(p); + let mut rng = SmallRng::from_seed( + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]); + for _ in 0..10 { + assert!(d.sample(&mut rng), "extremely unlikely to fail by accident"); + } +} diff --git a/crates/rand-0.5.0-pre.2/utils/ci/install.sh b/crates/rand-0.5.0-pre.2/utils/ci/install.sh new file mode 100644 index 0000000..8e636e1 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/utils/ci/install.sh @@ -0,0 +1,49 @@ +# From https://github.com/japaric/trust + +set -ex + +main() { + local target= + if [ $TRAVIS_OS_NAME = linux ]; then + target=x86_64-unknown-linux-musl + sort=sort + else + target=x86_64-apple-darwin + sort=gsort # for `sort --sort-version`, from brew's coreutils. + fi + + # Builds for iOS are done on OSX, but require the specific target to be + # installed. + case $TARGET in + aarch64-apple-ios) + rustup target install aarch64-apple-ios + ;; + armv7-apple-ios) + rustup target install armv7-apple-ios + ;; + armv7s-apple-ios) + rustup target install armv7s-apple-ios + ;; + i386-apple-ios) + rustup target install i386-apple-ios + ;; + x86_64-apple-ios) + rustup target install x86_64-apple-ios + ;; + esac + + # This fetches latest stable release + local tag=$(git ls-remote --tags --refs --exit-code https://github.com/japaric/cross \ + | cut -d/ -f3 \ + | grep -E '^v[0.1.0-9.]+$' \ + | $sort --version-sort \ + | tail -n1) + curl -LSfs https://japaric.github.io/trust/install.sh | \ + sh -s -- \ + --force \ + --git japaric/cross \ + --tag $tag \ + --target $target +} + +main diff --git a/crates/rand-0.5.0-pre.2/utils/ci/script.sh b/crates/rand-0.5.0-pre.2/utils/ci/script.sh new file mode 100644 index 0000000..21188f3 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/utils/ci/script.sh @@ -0,0 +1,29 @@ +# Derived from https://github.com/japaric/trust + +set -ex + +main() { + if [ ! -z $DISABLE_TESTS ]; then # tests are disabled + cross build --no-default-features --target $TARGET --release + if [ -z $DISABLE_STD ]; then # std is enabled + cross build --features log,serde1 --target $TARGET + fi + return + fi + + if [ ! -z $NIGHTLY ]; then # have nightly Rust + cross test --tests --no-default-features --features alloc --target $TARGET + cross test --package rand_core --no-default-features --features alloc --target $TARGET + cross test --features serde1,log,nightly,alloc --target $TARGET + cross test --all --benches --target $TARGET + else # have stable Rust + cross test --tests --no-default-features --target $TARGET + cross test --package rand_core --no-default-features --target $TARGET + cross test --features serde1,log --target $TARGET + fi +} + +# we don't run the "test phase" when doing deploys +if [ -z $TRAVIS_TAG ]; then + main +fi diff --git a/crates/rand-0.5.0-pre.2/utils/ziggurat_tables.py b/crates/rand-0.5.0-pre.2/utils/ziggurat_tables.py new file mode 100755 index 0000000..9973b83 --- /dev/null +++ b/crates/rand-0.5.0-pre.2/utils/ziggurat_tables.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python +# +# Copyright 2013 The Rust Project Developers. See the COPYRIGHT +# file at the top-level directory of this distribution and at +# https://rust-lang.org/COPYRIGHT. +# +# Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +# https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +# <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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 +// https://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or +// https://www.apache.org/licenses/LICENSE-2.0%3E or the MIT license +// <LICENSE-MIT or https://opensource.org/licenses/MIT%3E, 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))