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commit 4fa174e342b92a58ddb0517ecf69860029cf33a6 Author: kernelmethod <17100608+kernelmethod@users.noreply.github.com> Date: Mon Jan 20 12:15:55 2020 -0700 Add TagBot.yml, and set Project.toml version to 0.0.1-alpha. commit 3ef089ac5371accda71b11550d50bf39c201f5fe Author: kernelmethod <17100608+kernelmethod@users.noreply.github.com> Date: Sun Jan 19 17:43:39 2020 -0700 Fix arXiv links. commit b1b3fc284079dbd266ccb401c39bd36c9821b1d4 Merge: 50d1a85 dda1180 Author: kernelmethod <17100608+kernelmethod@users.noreply.github.com> Date: Sun Jan 19 17:41:19 2020 -0700 Merge branch 'master' into docs commit 50d1a855571e8afc9351043af57dc20f30252bea Author: kernelmethod <17100608+kernelmethod@users.noreply.github.com> Date: Sat Jan 18 14:20:39 2020 -0700 Add a missing parenthesis on the home page.
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.github/workflows/TagBot.yml

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name: TagBot
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on:
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schedule:
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- cron: 0 * * * *
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jobs:
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TagBot:
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runs-on: ubuntu-latest
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steps:
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- uses: JuliaRegistries/TagBot@v1
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with:
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token: ${{ secrets.GITHUB_TOKEN }}

Project.toml

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name = "LSH"
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uuid = "5134c85a-a9db-11e9-340f-8514dff59a31"
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authors = ["Will Shand <wish5031@colorado.edu>"]
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version = "0.1.0"
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version = "0.0.1-alpha"
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[deps]
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Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

docs/src/index.md

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LSH.jl is a package that provides definitions of locality-sensitive hash functions for a variety of different similarities. Currently, LSH.jl supports hash functions for
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- Cosine similarity ([`cossim`](@ref))
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- Jaccard similarity ([`jaccard`](@ref)
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- Jaccard similarity ([`jaccard`](@ref))
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- ``\ell^1`` (Manhattan / "taxicab") distance ([`ℓ1`](@ref))
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- ``\ell^2`` (Euclidean) distance ([`ℓ2`](@ref))
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- Inner product ([`inner_prod`](@ref))

src/hashes/mips_hash.jl

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```
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# References
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- Anshumali Shrivastava and Ping Li. *Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)*. Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, page 2321–2329, Cambridge, MA, USA, 2014. MIT Press. 10.5555/2969033.2969086. [https://arxiv.org/abs/1405.5869](https://arxiv.org/abs/1405.5869)
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- Anshumali Shrivastava and Ping Li. *Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)*. Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, page 2321–2329, Cambridge, MA, USA, 2014. MIT Press. 10.5555/2969033.2969086. [arXiv:1410.5410](https://arxiv.org/abs/1405.5869)
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See also: [`inner_prod`](@ref), [`ℓ2_norm`](@ref ℓp_norm)
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"""

src/hashes/sign_alsh.jl

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```
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# References
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- Anshumali Shrivastava and Ping Li. *Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)*. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI'15, page 812–821, Arlington, Virginia, USA, 2015. AUAI Press. 10.5555/3020847.3020931. [https://arxiv.org/abs/1410.5410v2](https://arxiv.org/abs/1410.5410v2)
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- Anshumali Shrivastava and Ping Li. *Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)*. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI'15, page 812–821, Arlington, Virginia, USA, 2015. AUAI Press. 10.5555/3020847.3020931. [arXiv:1405.5869](https://arxiv.org/abs/1410.5410v2)
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See also: [`inner_prod`](@ref inner_prod(::AbstractVector, ::AbstractVector)), [`ℓ2_norm`](@ref ℓp_norm)
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"""

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