v0.1.7 beta release

Author: PyDataBlog

README.md

@@ -65,7 +65,8 @@ ________________________________________________________________________________
 - Lightening fast implementation of K-Means clustering algorithm even on a single thread in native Julia.
 - Support for multi-theading implementation of K-Means clustering algorithm.
 - Kmeans++ initialization for faster and better convergence.
-- Implementation of all the variants of the K-Means algorithm.
+- Implementation of all available variants of the K-Means algorithm.
+- Support for all distance metrics available at [Distances.jl](https://github.com/JuliaStats/Distances.jl)
 - Supported interface as an [MLJ](https://github.com/alan-turing-institute/MLJ.jl#available-models) model.
 
 _________________________________________________________________________________________________________

docs/src/index.md

@@ -210,7 +210,7 @@ ________________________________________________________________________________
 - 0.1.4 Bug fixes.
 - 0.1.5 Added `Yinyang` algorithm.
 - 0.1.6 Added support for weighted k-means; Added `Coreset` algorithm; improved support for different types of the design matrix.
-- 0.1.7 Added `Yinyang` and `Coreset` support in MLJ interface; added `weights` support in MLJ; added RNG seed support in MLJ interface and through all algorithms.
+- 0.1.7 Added `Yinyang` and `Coreset` support in MLJ interface; added `weights` support in MLJ; added RNG seed support in MLJ interface and through all algorithms; added metric support.
 
 ## Contributing
 

test/test07_coreset.jl

@@ -69,7 +69,7 @@ end
 
     @test res.converged == baseline.converged
     @test res.iterations == baseline.iterations
-    @test floor(res.totalcost) == floor(baseline.totalcost)
+    @test (res.totalcost - baseline.totalcost) ≤ 1
 
 end