Rename project to LSHFunctions.jl.

Author: kernelmethod

Project.toml

@@ -1,4 +1,4 @@
-name = "LSH"
+name = "LSHFunctions"
 uuid = "5134c85a-a9db-11e9-340f-8514dff59a31"
 authors = ["Will Shand <wish5031@colorado.edu>"]
 version = "0.0.1"

README.md

@@ -1,9 +1,9 @@
-# LSH.jl
+# LSHFunctions.jl
 
-- Docs: [![Stable docs](https://img.shields.io/badge/docs-stable-blue.svg)](https://kernelmethod.github.io/LSH.jl/stable/) [![Dev docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://kernelmethod.github.io/LSH.jl/dev/)
-- Build status: [![Build Status](https://travis-ci.com/kernelmethod/LSH.jl.svg?branch=master)](https://travis-ci.com/kernelmethod/LSH.jl)
-- Code coverage: [![Coverage Status](https://coveralls.io/repos/github/kernelmethod/LSH.jl/badge.svg?branch=master)](https://coveralls.io/github/kernelmethod/LSH.jl?branch=master)
-[![codecov](https://codecov.io/gh/kernelmethod/LSH.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/kernelmethod/LSH.jl)
+- Docs: [![Stable docs](https://img.shields.io/badge/docs-stable-blue.svg)](https://kernelmethod.github.io/LSHFunctions.jl/stable/) [![Dev docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://kernelmethod.github.io/LSHFunctions.jl/dev/)
+- Build status: [![Build Status](https://travis-ci.com/kernelmethod/LSHFunctions.jl.svg?branch=master)](https://travis-ci.com/kernelmethod/LSHFunctions.jl)
+- Code coverage: [![Coverage Status](https://coveralls.io/repos/github/kernelmethod/LSHFunctions.jl/badge.svg?branch=master)](https://coveralls.io/github/kernelmethod/LSHFunctions.jl?branch=master)
+[![codecov](https://codecov.io/gh/kernelmethod/LSHFunctions.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/kernelmethod/LSHFunctions.jl)
 - DOI to cite this code: [![DOI](https://zenodo.org/badge/197700982.svg)](https://zenodo.org/badge/latestdoi/197700982)
 
 A Julia package for [locality-sensitive hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) to accelerate similarity search.
@@ -22,10 +22,10 @@ Traditionally, if you have a data point `x`, and want to find the most similar p
 **Locality-sensitive hashing** (LSH) is a technique for accelerating these kinds of similarity searches. Instead of measuring how similar your query point is to every point in your database, you calculate a few hashes of the query point and only compare it against those points with which it experiences a hash collision. Locality-sensitive hash functions are randomly generated, with the fundamental property that as the similarity between `x` and `y` increases, the probability of a hash collision between `x` and `y` also increases.
 
 ## Installation
-You can install LSH.jl from the Julia REPL with
+You can install LSHFunctions.jl from the Julia REPL with
 
 ```
-pkg> add https://github.com/kernelmethod/LSH.jl
+pkg> add https://github.com/kernelmethod/LSHFunctions.jl
 ```
 
 ## Supported similarity functions
@@ -75,4 +75,4 @@ julia> x = randn(128);
 julia> x_hashes = hashfn(x);
 ```
 
-For more details, [check out the LSH.jl documentation](https://kernelmethod.github.io/LSH.jl/dev/).
+For more details, [check out the LSHFunctions.jl documentation](https://kernelmethod.github.io/LSHFunctions.jl/dev/).

docs/README.md

@@ -1,5 +1,5 @@
-# LSH.jl documentation
-Documentation for the LSH.jl package.
+# LSHFunctions.jl documentation
+Documentation for the LSHFunctions.jl package.
 
 The module documentation is automatically built and updated whenever `master` is updated. To develop the documentation locally, run
 
@@ -12,5 +12,5 @@ $ python3 -m http.server 8000
 
 Then go to [http://localhost:8000/build/](http://localhost:8000/build/) in your browser.
 
-- [Stable docs](https://kernelmethod.github.io/LSH.jl/stable/)
-- [Developer docs](https://kernelmethod.github.io/LSH.jl/dev/)
+- [Stable docs](https://kernelmethod.github.io/LSHFunctions.jl/stable/)
+- [Developer docs](https://kernelmethod.github.io/LSHFunctions.jl/dev/)

docs/make.jl

@@ -8,7 +8,7 @@ pushfirst!(LOAD_PATH, joinpath(@__DIR__, ".."))
 using Documenter, LSH
 
 makedocs(
-    sitename = "LSH.jl",
+    sitename = "LSHFunctions.jl",
     format   = Documenter.HTML(),
     modules  = [LSH],
     pages    = ["Home" => "index.md",
@@ -25,5 +25,5 @@ makedocs(
 )
 
 deploydocs(
-    repo = "github.com/kernelmethod/LSH.jl.git"
+    repo = "github.com/kernelmethod/LSHFunctions.jl.git"
 )

docs/src/index.md

@@ -1,6 +1,6 @@
-# LSH.jl
+# LSHFunctions.jl
 
-LSH.jl is a Julia package for performing [locality-sensitive hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) with various similarity functions.
+LSHFunctions.jl is a Julia package for performing [locality-sensitive hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) with various similarity functions.
 
 ## Introduction
 One of the simplest methods for classifying, categorizing, and grouping data is to measure how similar pairs of data points are. For instance, the classical [``k``-nearest neighbors algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) searches an input space ``X`` by taking a query point ``x\in X`` and a similarity function
@@ -19,7 +19,7 @@ Broadly, there are two computational issues with this approach:
 ## Locality-sensitive hashing
 *Locality-sensitive hashing* (LSH) is a technique for accelerating similarity search that works by using a hash function on the query point ``x`` and limiting similarity search to only those points in the database that experience a hash collision with ``x``. The hash functions that are used are randomly generated from a family of *locality-sensitive hash functions*. These hash functions have the property that ``Pr[h(x) = h(y)]`` (i.e., the probability of a hash collision) increases the more similar that ``x`` and ``y`` are.
 
-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
+LSHFunctions.jl is a package that provides definitions of locality-sensitive hash functions for a variety of different similarities. Currently, LSHFunctions.jl supports hash functions for
 
 - Cosine similarity ([`cossim`](@ref))
 - Jaccard similarity ([`jaccard`](@ref))

docs/src/lshfunction_api.md

@@ -1,7 +1,7 @@
 # The LSHFunction API
 
 !!! warning "Under construction"
-    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSH.jl/pulls); otherwise, please check back later.
+    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSHFunctions.jl/pulls); otherwise, please check back later.
 
 ## LSHFunction
 The `LSH` module exposes a relatively easy interface for constructing new hash functions. Namely, you call [`LSHFunction`](@ref) with 
@@ -73,7 +73,7 @@ L1Hash
 ```
 
 ## Utilities
-LSH.jl provides a few common utility functions that you can use across [`LSHFunction`](@ref) subtypes:
+LSHFunctions.jl provides a few common utility functions that you can use across [`LSHFunction`](@ref) subtypes:
 
 - [`n_hashes`](@ref): returns the number of hash functions computed by an [`LSHFunction`](@ref).
 

docs/src/performance.md

@@ -1,4 +1,4 @@
 # Performance tips
 
 !!! warning "Under construction"
-    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSH.jl/pulls); otherwise, please check back later.
+    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSHFunctions.jl/pulls); otherwise, please check back later.

docs/src/similarities/cosine.md

@@ -1,7 +1,7 @@
 # Cosine similarity
 
 !!! warning "Under construction"
-    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSH.jl/pulls); otherwise, please check back later.
+    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSHFunctions.jl/pulls); otherwise, please check back later.
 
 ## Definition
 [Cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity), roughly speaking, measures the angle between a pair of inputs. Two inputs are very similar if the angle between them is low, and their similarity drops as the angle between them increases.

docs/src/similarities/inner_prod.md

@@ -1,4 +1,4 @@
 # Inner product similarity
 
 !!! warning "Under construction"
-    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSH.jl/pulls); otherwise, please check back later.
+    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSHFunctions.jl/pulls); otherwise, please check back later.

docs/src/similarities/jaccard.md

@@ -1,4 +1,4 @@
 # Jaccard similarity
 
 !!! warning "Under construction"
-    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSH.jl/pulls); otherwise, please check back later.
+    This section is currently being developed. If you're interested in helping write this section, feel free to [open a pull request](https://github.com/kernelmethod/LSHFunctions.jl/pulls); otherwise, please check back later.