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[tor-commits] [tor-rust-dependencies/master] Update rand dependency to 0.5.0-pre.2.
commit c01ff7f539a5643a607b8c156032cef48c6fcff8
Author: Isis Lovecruft <isis@xxxxxxxxxxxxxx>
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 @@
+{"files":{".travis.yml":"92eca358a96aca08b5c75579a208f78b01672fcf74fb87040e52a0780e66ce21","CHANGELOG.md":"c051ed3322ff4381b518c90d027498fefe338fa312db85242cfe902c00bc14a7","CONTRIBUTING.md":"ce1d116f2991f195479a217ecad883e65812d739476ce313adbbed16425e9730","Cargo.toml":"d4451f3210b7668d1697e0cfc59c9b1c2c0b3811e805d7557672ac3a65a0f6ff","LICENSE-APACHE":"aaff376532ea30a0cd5330b9502ad4a4c8bf769c539c87ffe78819d188a18ebf","LICENSE-MIT":"6485b8ed310d3f0340bf1ad1f47645069ce4069dcc6bb46c7d5c6faf41de1fdb","README.md":"97ae1d8ed0bddde7657e6fdf36db2f46df10d2028f26217c58572827699121fe","UPDATING.md":"995b503e4e25662c63df1710ca557e9688e1095644a4de80c6d19a2aa4281f2d","appveyor.yml":"e72b9bf8fa81ec7bc8939da12f51df6de946b93246542cf1f538ff1119497de6","benches/distributions.rs":"aa379a1a47c60078dba4510aab34011912af92dcaa4f9698a733498875359502","benches/generators.rs":"68d52b3ae51d8c775f35c741eb10e00c124c6fc76a4af9b07e1c5899ee0e5338","benches/misc.rs":"6d4debf1467810f3d4025de7a1de26b92e8b2ab45b07f1a1
5ed281142a8d2455","examples/monte-carlo.rs":"5b52e0dff8b2c29b2037c1f93f703e1c203d05b884640db99520137307fd9d6e","examples/monty-hall.rs":"d4873d567a606822dcb2be7e06517144f44d0d0ec39990751756b6265acaa88c","src/distributions/bernoulli.rs":"3a8dbab7dc97e6ad8d3225857dd6413e44a78d49ff870c25b1cd2c3bf7fdb6c3","src/distributions/binomial.rs":"29f3834d2de182e4c5d7359191242d728ee31f40a86dbf8fc5a408f634b3a736","src/distributions/exponential.rs":"b4ab0710a1c600c50503772c17be5d66271d20a4ef82c5789a7dad51f1fe0c9e","src/distributions/float.rs":"0d0e79423d002a3187a7dec73e11e5acd9060590e71a3e39ca44375cafc687d4","src/distributions/gamma.rs":"cad9630b2e3afae251a5041aa6afdc3813bda8b05a0d7f1e0fa25e65d09ad714","src/distributions/integer.rs":"a9c9edda09a457831c06e93ce94e7074af5bf97fd663a940b2f09ea21b1106a9","src/distributions/log_gamma.rs":"9f1fda43162c3f6184af15e9fabf3475de7e4e2e46412e7ae1480de1c7e5a68e","src/distributions/mod.rs":"fb1a822856d8da72e424e9e107059d0b341c34f8f638c82bd5594ea4eda14e3e","src/dist
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gs/small.rs":"a6d51c0066e225bcab8a563b26a0f1b8a555ef1e3aca20210b6cd38513f08c8a","src/rngs/std.rs":"a38628a9bb2ed68daa9f9288380f274713f035b27f287e67a310c5bb4cb14f97","src/rngs/thread.rs":"a10c670e9e19a9c4423c3b4c17a76029165c01c3367a01f94b9493b0eab0c6f1","src/seq.rs":"f9d825660f9ef0594a99ceb80ab32ad89b801c99daebb5e8ba0a61379d027687","tests/bool.rs":"b57dd7c6e597383f7f84a172cd29e85246475f2686159ca6aa61c52989f762ba","utils/ci/install.sh":"cbc29da047c6e516ff5d13731c8b07812fd40986e7b19b61a7c5deaf5d6ffdd0","utils/ci/script.sh":"309a982ecc8f5669cc1a9d0d67e0f743f3969f1a5a49e8285966ab5518767c43","utils/ziggurat_tables.py":"97d156574ad51465dd023ee63a3fbb52a5603963b09f3f30a50cdfd2bbf1a1e1"},"package":"3795e4701d9628a63a84d0289e66279883b40df165fca7caed7b87122447032a"}
\ No newline at end of file
diff --git a/crates/rand-0.5.0-pre.2/.travis.yml b/crates/rand-0.5.0-pre.2/.travis.yml
new file mode 100644
index 0000000..7d3633b
--- /dev/null
+++ b/crates/rand-0.5.0-pre.2/.travis.yml
@@ -0,0 +1,113 @@
+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/
+
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diff --git a/crates/rand-0.5.0-pre.2/LICENSE-MIT b/crates/rand-0.5.0-pre.2/LICENSE-MIT
new file mode 100644
index 0000000..39d4bdb
--- /dev/null
+++ b/crates/rand-0.5.0-pre.2/LICENSE-MIT
@@ -0,0 +1,25 @@
+Copyright (c) 2014 The Rust Project Developers
+
+Permission is hereby granted, free of charge, to any
+person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the
+Software without restriction, including without
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+publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software
+is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice
+shall be included in all copies or substantial portions
+of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
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+TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
diff --git a/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
+
+[](https://travis-ci.org/rust-lang-nursery/rand)
+[](https://ci.appveyor.com/project/alexcrichton/rand)
+[](https://crates.io/crates/rand)
+[](https://docs.rs/rand)
+[](https://github.com/rust-lang-nursery/rand#rust-version-requirements)
+
+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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! # Monte Carlo estimation of Ï?
+//!
+//! 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 = Ï?r² = Ï? * r * r = Ï?
+//! area of square = 2² = 4
+//! ```
+//!
+//! The circle is entirely within the square, so if we sample many points
+//! randomly from the square, roughly Ï? / 4 of them should be inside the circle.
+//!
+//! We can use the above fact to estimate the value of Ï?: 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!("Ï? 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! ## 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+//! The Bernoulli distribution.
+
+use Rng;
+use distributions::Distribution;
+
+/// The Bernoulli distribution.
+///
+/// This is a special case of the Binomial distribution where `n = 1`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Bernoulli, Distribution};
+///
+/// let d = Bernoulli::new(0.3);
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a Bernoulli distribution", v);
+/// ```
+///
+/// # Precision
+///
+/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
+/// so only probabilities that are multiples of 2<sup>-64</sup> can be
+/// represented.
+#[derive(Clone, Copy, Debug)]
+pub struct Bernoulli {
+ /// Probability of success, relative to the maximal integer.
+ p_int: u64,
+}
+
+impl Bernoulli {
+ /// Construct a new `Bernoulli` with the given probability of success `p`.
+ ///
+ /// # Panics
+ ///
+ /// If `p < 0` or `p > 1`.
+ ///
+ /// # Precision
+ ///
+ /// For `p = 1.0`, the resulting distribution will always generate true.
+ /// For `p = 0.0`, the resulting distribution will always generate false.
+ ///
+ /// This method is accurate for any input `p` in the range `[0, 1]` which is
+ /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
+ /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
+ #[inline]
+ pub fn new(p: f64) -> Bernoulli {
+ 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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
+ /// [2<sup>0</sup>..2<sup>1</sup>), 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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) / (Î?(k) * θ^k)
+/// ```
+///
+/// where `Î?` 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 `Ï?²(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
+/// `Ï?²(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 Ï?²(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) = (Ï?²(m)/m) /
+/// (Ï?²(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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+/// 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+// 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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 Ï?](
+//! https://github.com/rust-lang-nursery/rand/blob/master/examples/monte-carlo.rs)
+//! - [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 +Â ?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 [Fisherâ??Yates shuffle](
+ /// https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
+ /// 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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 2<sup>64</sup> 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 (2<sup>4</sup>) 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; 2<sup>64</sup> 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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 2<sup>128</sup>. 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<sup>1024*32+10-1</sup> = 2<sup>32777</sup>.
+/// 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. 39â??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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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 = IsaacArray<Self::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 IsaacArray<Self::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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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 = IsaacArray<Self::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 IsaacArray<Self::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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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 | â??â??â??â??â?? | 16 bytes | â??â??â??â??â?? | `u32` * 2<sup>128</sup> - 1 | â?? |
+//!
+// 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 | â??â??â??â??â?? | fast | 136 bytes | secure | no |
+//! | [`Hc128Rng`] | HC-128 | â??â??â??â??â?? | slow | 4176 bytes | secure | no |
+//! | [`IsaacRng`] | ISAAC | â??â??â??â??â?? | slow | 2072 bytes | unknown | unknown |
+//! | [`Isaac64Rng`] | ISAAC-64 | â??â??â??â??â?? | 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*
+//! 2<sup>64</sup> can be used for centuries before cycling. Yet we recommend a
+//! period of 2<sup>128</sup> 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 2<sup>128</sup>, and an application consumes
+//! 2<sup>48</sup> values, it then takes about 2<sup>32</sup> 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
+//! 2<sup>128</sup>. 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 2<sup>64</sup> values for a
+//! period of 2<sup>128</sup>).
+//!
+//!
+//! # 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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 2<sup>64</sup>. Would reseeding help to ensure that you do
+/// not wrap around at the end of the period? A period of 2<sup>64</sup> 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+//
+// Based on jitterentropy-library, http://www.chronox.de/jent.html.
+// Copyright Stephan Mueller <smueller@xxxxxxxxxx>, 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 10<sup>3</sup>..10<sup>6</sup> 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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 â?? 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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` â??may return data prior to the
+/// entropy pool being initializedâ??. 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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 â?? 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 â?? 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! 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> or the MIT license
+# <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+# option. This file may not be copied, modified, or distributed
+# except according to those terms.
+
+# 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> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+// 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))
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