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The current example to implement `SampleUniform` for custom types is incorrect. It mistakenly forwards the implementation of `UniformSampler::new_inclusive` to `UniformSampler::new`.
rand: fix incorrect example in Uniform
…ed for Lcg128CmDxsm64.
Add Lcg128CmDxsm64 generator compatible with NumPy's PCG64DXSM
Generating Dirichlet samples using the method based on samples from the gamma distribution can result in samples being nan if all the values in alpha are sufficiently small. The fix is to instead use the method based on the marginal distributions being the beta distribution (i.e. the "stick breaking" method) when all values in alpha are small.
rand_distr: fix no_std build
Remove unused `slice_partition_at_index` feature
Update to packed_simd_2 0.3.7
Making distributions comparable by deriving PartialEq
Mention that `Fill` supports floats
This breaks serialization compatibility with older versions.
Signed-off-by: cuishuang <[email protected]>
Enhance Kolmogorov–Smirnov Test Coverage for Various Distributions
Closes #1358 by documenting what Rand is not. Co-authored-by: Dan <[email protected]>
Adds random_iter, random_range, random_bool, random_ratio, fill. See also #989, #1503. Co-authored-by: Diggory Hardy <[email protected]>
Adds new non-publishing distr_test crate
The `rng` parameter seems redundant in `ReseedingRng::new` (aside from the type specification).
- Extra testing for weighted sampling - Fix IndexedRandom::choose_multiple_weighted with very small keys - Use A-ExpJ algorithm with BinaryHeap for better performance with large length / amount
- Update version numbers - Update CHANGELOGs - Add an extra benchmark
- Move `Slice` -> `slice::Choose`, `EmptySlice` -> `slice::Empty`
- Rename trait `DistString` -> `SampleString`
- Rename `DistIter` -> `Iter`, `DistMap` -> `Map`
- Move `{Weight, WeightError, WeightedIndex}` -> `weighted::{Weight,
Error, WeightedIndex}`
- Move `weighted_alias::{AliasableWeight, WeightedAliasIndex}` ->
`weighted::{..}`
- Move `weighted_tree::WeightedTreeIndex` ->
`weighted::WeightedTreeIndex`
- [x] Added a `CHANGELOG.md` entry # Summary As discussed in #1515, this PR replaces the implementation of `poisson::RejectionMethod` with a new algorithm based on the [paper ](https://dl.acm.org/doi/10.1145/355993.355997). # Motivation The new implementation offers improved performance and maintains better sampling distribution, especially for extreme values of lambda (> 1e9). # Details In terms of performance, here are the benchmarks I ran, with the current implementation as the baseline: ```text poisson/100 time: [45.5242 cycles 45.6734 cycles 45.8337 cycles] change: [-86.572% -86.507% -86.438%] (p = 0.00 < 0.05) Performance has improved. Found 5 outliers among 100 measurements (5.00%) 2 (2.00%) low mild 2 (2.00%) high mild 1 (1.00%) high severe poisson/variable time: [5494.6626 cycles 5508.2882 cycles 5523.2298 cycles] thrpt: [5523.2298 cycles/100 5508.2882 cycles/100 5494.6626 cycles/100] change: time: [-76.728% -76.573% -76.430%] (p = 0.00 < 0.05) thrpt: [+324.27% +326.85% +329.69%] Performance has improved. Found 5 outliers among 100 measurements (5.00%) 1 (1.00%) low mild 3 (3.00%) high mild 1 (1.00%) high severe ```
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