initiated refactoring of 0.1.0 release

Author: PyDataBlog

src/ParallelKMeans.jl

@@ -6,44 +6,37 @@ import Base.Threads: @spawn, @threads
 export kmeans
 
 
+# All Abstract types defined 
+"""
+    TODO: Docs
+"""
 abstract type AbstractKMeansAlg end
-abstract type CalculationMode end
 
-struct Lloyd <: AbstractKMeansAlg end
-struct LightElkan <: AbstractKMeansAlg end
-
-# Single thread class to control the calculation type based on the CalculationMode
-struct SingleThread <: CalculationMode
-end
-
-# Multi threaded implementation to control the calculation type based avaialble threads
-struct MultiThread <: CalculationMode
-    n::Int
-end
-
-# Get the number of avaialble threads for multithreading implementation
-MultiThread() = MultiThread(Threads.nthreads())
+"""
+    TODO: Docs
+"""
+abstract type CalculationMode end
 
-# TODO here we mimic `Clustering` data structure, should thing how to integrate these
-# two packages more closely.
 
 """
     ClusteringResult
 Base type for the output of clustering algorithm.
 """
 abstract type ClusteringResult end
 
-# C is the type of centers, an (abstract) matrix of size (d x k)
-# D is the type of pairwise distance computation from points to cluster centers
-# WC is the type of cluster weights, either Int (in the case where points are
-# unweighted) or eltype(weights) (in the case where points are weighted).
+
+# Here we mimic `Clustering` output structure
 """
     KmeansResult{C,D<:Real,WC<:Real} <: ClusteringResult
 The output of [`kmeans`](@ref) and [`kmeans!`](@ref).
 # Type parameters
  * `C<:AbstractMatrix{<:AbstractFloat}`: type of the `centers` matrix
  * `D<:Real`: type of the assignment cost
  * `WC<:Real`: type of the cluster weight
+ # C is the type of centers, an (abstract) matrix of size (d x k)
+# D is the type of pairwise distance computation from points to cluster centers
+# WC is the type of cluster weights, either Int (in the case where points are
+# unweighted) or eltype(weights) (in the case where points are weighted).
 """
 struct KmeansResult{C<:AbstractMatrix{<:AbstractFloat},D<:Real,WC<:Real} <: ClusteringResult
     centers::C                 # cluster centers (d x k)
@@ -56,6 +49,32 @@ struct KmeansResult{C<:AbstractMatrix{<:AbstractFloat},D<:Real,WC<:Real} <: Clus
     converged::Bool            # whether the procedure converged
 end
 
+# All Structs/Classess defined
+"""
+    TODO: Docs
+"""
+struct Lloyd <: AbstractKMeansAlg end
+
+
+"""
+    TODO: Docs
+"""
+struct LightElkan <: AbstractKMeansAlg end
+
+# Single thread class to control the calculation type based on the CalculationMode
+struct SingleThread <: CalculationMode
+end
+
+# Multi threaded implementation to control the calculation type based avaialble threads
+struct MultiThread <: CalculationMode
+    n::Int
+end
+
+# Get the number of avaialble threads for multithreading implementation
+MultiThread() = MultiThread(Threads.nthreads())
+
+
+
 """
     colwise!(target, x, y, mode)