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Ensure Zipf always returns values in range - #57
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vks
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| #[test] | ||
| fn zipf_sample() { | ||
| let d = Zipf::new(10., 0.5).unwrap(); | ||
| let mut rng = crate::test::rng(2); | ||
| for _ in 0..1000 { | ||
| let r = d.sample(&mut rng); | ||
| assert!(r >= 1.); | ||
| } | ||
| } | ||
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| #[test] | ||
| fn zipf_sample_s_1() { | ||
| let d = Zipf::new(10., 1.).unwrap(); | ||
| let mut rng = crate::test::rng(2); | ||
| for _ in 0..1000 { | ||
| let r = d.sample(&mut rng); | ||
| assert!(r >= 1.); | ||
| } | ||
| } |
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Could you please add assert!(r <= 10.)?
I think it makes sense to test the same invariant here that is now part of the fuzzing.
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Sure, I've added the check.
Due to floating point error, it was possible for `x` to exceed `n` even with small values of `n`.
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CHANGELOG.mdentrySummary
This updates the Zipf sampler to always produce values in the range [1,n].
Motivation
This should fix #54.
Details
Due to floating point error, it was possible for
xto exceedneven with small values ofn. Updating the calculations producingxto avoid that error entirely would probably require somewhat complicated biasing and rounding tricks, so it is easier to just clip the output at the end. I've explicitly updatedxto be<= n.floor()instead of just<= nto ensure the output remains integral even ifnis not; this is compatible with the documentation of the Zipf sampler and matches non-erroneous current behavior. Because the overflow only occurs rarely (when the random sample is close to 1) this should have negligible effect on the distribution.