Merge pull request #59 from PyDataBlog/experimental

Release of v0.1.3

Author: PyDataBlog

Project.toml

@@ -1,7 +1,7 @@
 name = "ParallelKMeans"
 uuid = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 authors = ["Bernard Brenyah", "Andrey Oskin"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

docs/src/benchmark_image.png

@@ -1,9 +1,5 @@
 # [ParallelKMeans.jl Package](https://github.com/PyDataBlog/ParallelKMeans.jl)
 
-```@contents
-Depth = 4
-```
-
 ## Motivation
 
 It's actually a funny story led to the development of this package.
@@ -61,13 +57,14 @@ git checkout experimental
 - [X] Interface for inclusion in Alan Turing Institute's [MLJModels](https://github.com/alan-turing-institute/MLJModels.jl#who-is-this-repo-for).
 - [X] Full Implementation of Triangle inequality based on [Elkan - 2003 Using the Triangle Inequality to Accelerate K-Means"](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf).
 - [ ] Implementation of [Geometric methods to accelerate k-means algorithm](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf).
+- [ ] Support for other distance metrics supported by [Distances.jl](https://github.com/JuliaStats/Distances.jl#supported-distances).
 - [ ] Native support for tabular data inputs outside of MLJModels' interface.
 - [ ] Refactoring and finalizaiton of API desgin.
 - [ ] GPU support.
-- [ ] Even faster Kmeans implementation based on recent literature.
+- [ ] Implementation of other K-Means algorithm variants based on recent literature.
 - [ ] Optimization of code base.
 - [ ] Improved Documentation
-- [ ] More benchmark tests
+- [ ] More benchmark tests.
 
 ## How To Use
 
@@ -83,7 +80,7 @@ multi_results = kmeans(X, 3; max_iters=300)
 results = kmeans(X, 3; n_threads=1, max_iters=300)
 ```
 
-The main design goal is to offer all available variations of the KMeans algorithm to end users as composable elements. By default, Lloyd's implementation is used but users can specify different variations of the KMeans clustering algorithm via this interface
+The main design goal is to offer all available variations of the KMeans algorithm to end users as composable elements. By default, Lloyd's implementation is used but users can specify different variations of the KMeans clustering algorithm via this interface;
 
 ```julia
 some_results = kmeans([algo], input_matrix, k; kwargs)
@@ -105,8 +102,8 @@ r.converged             # whether the procedure converged
 
 - [Lloyd()](https://cs.nyu.edu/~roweis/csc2515-2006/readings/lloyd57.pdf)
 - [Hamerly()](https://www.researchgate.net/publication/220906984_Making_k-means_Even_Faster)
+- [Elkan()](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf)
 - [Geometric()](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf) - (Coming soon)
-- [Elkan()](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf) - (Coming soon)
 - [MiniBatch()](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) - (Coming soon)
 
 ### Practical Usage Examples
@@ -162,22 +159,23 @@ Currently, the benchmark speed tests are based on the search for optimal number
 
 _________________________________________________________________________________________________________
 
-| 1 million (ms) | 100k (ms) | 10k (ms) | 1k (ms) | package                 | language |
-|:--------------:|:---------:|:--------:|:-------:|:-----------------------:|:--------:|
-| 600184.00      | 31959.00  | 832.25   | 18.19   | Clustering.jl           | Julia    |
-| 35733.00       | 4473.00   | 255.71   | 8.94    | Lloyd                   | Julia    |
-| 12617.00       | 1655.00   | 122.53   | 7.98    | Hamerly                 | Julia    |
-| 1430000.00     | 146000.00 | 5770.00  | 344.00  | Sklearn Kmeans          | Python   |
-| 30100.00       | 3750.00   | 613.00   | 201.00  | Sklearn MiniBatchKmeans | Python   |
-| 218200.00      | 15510.00  | 733.70   | 19.47   | Knor                    | R        |
-
+|1 million (ms)|100k (ms)|10k (ms)|1k (ms)|package                |language|
+|:------------:|:-------:|:------:|:-----:|:---------------------:|:------:|
+|    666840    |  34034  |709.049 |17.686 |     Clustering.jl     | Julia  |
+|    21730     |  2975   |163.771 | 6.444 | ParallelKMeans Lloyd  | Julia  |
+|    11784     |  1339   | 94.233 |  6.6  |ParallelKMeans Hamerly | Julia  |
+|    17591     |  1074   | 81.995 | 6.953 | ParallelKMeans Elkan  | Julia  |
+|   1430000    | 146000  |  5770  |  344  |    Sklearn Kmeans     | Python |
+|    30100     |  3750   |  613   |  201  |Sklearn MiniBatchKmeans| Python |
+|    218200    |  15510  | 733.7  | 19.47 |         Knor          |   R    |
 _________________________________________________________________________________________________________
 
 ## Release History
 
-- 0.1.0 Initial release
-- 0.1.1 Added interface for MLJ
-- 0.1.2 Added Elkan algorithm
+- 0.1.0 Initial release.
+- 0.1.1 Added interface for MLJ.
+- 0.1.2 Added Elkan algorithm.
+- 0.1.3 Faster & optimized execution.
 
 ## Contributing