update updated_kmeans.jl

Added speed benchmark tests.

Author: PyDataBlog

Tutorials/kmeans_implementation_project/updated_kmeans.jl

@@ -1,10 +1,12 @@
+using Pkg
 # Replace python environment to suit your needs
 ENV["PYTHON"] = "/home/mysterio/miniconda3/envs/pydata/bin/python"
 Pkg.build("PyCall")  # Build PyCall to suit the specified Python env
 
 using PyCall
 using Plots
 using LinearAlgebra
+using Statistics
 using BenchmarkTools
 using Distances
 
@@ -26,12 +28,12 @@ ran_c = randn(ran_k, ran_k)
 """
 function sum_of_squares(x::Array{Float64,2}, labels::Array{Int64,1}, centre::Array, k::Int)
     ss = 0
-    
+
     for j = 1:k
         group_data = x[findall(labels .== j), :]
         group_centroid_matrix  = convert(Matrix, centre[j, :]')
         group_distance = pairwise(Euclidean(), group_data, group_centroid_matrix, dims=1)
-        
+
         ss += sum(group_distance .^ 2)
     end
 
@@ -56,16 +58,16 @@ function Kmeans(design_matrix::Array{Float64, 2}, k::Int64; max_iters::Int64=300
     distances = zeros(n_row)
 
     J_previous = Inf64
-    
+
     # Update centroids & labels with closest members until convergence
     for iter = 1:max_iters
         nearest_neighbour = pairwise(Euclidean(), design_matrix, centroids, dims=1)
-        
+
         min_val_idx = findmin.(eachrow(nearest_neighbour))
 
         distances = [x[1] for x in min_val_idx]
         labels = [x[2] for x in min_val_idx]
-        
+
         centroids = [ mean( X[findall(labels .== j), : ], dims = 1) for j = 1:k]
         centroids = reduce(vcat, centroids)
 
@@ -75,7 +77,7 @@ function Kmeans(design_matrix::Array{Float64, 2}, k::Int64; max_iters::Int64=300
             # Show progress and terminate if J stopped decreasing.
             println("Iteration ", iter, ": Jclust = ", J, ".")
         end;
-        
+
         # Final Step 5: Check for convergence
         if iter > 1 && abs(J - J_previous) < (tol * J)
             # TODO: Calculate the sum of squares
@@ -84,9 +86,9 @@ function Kmeans(design_matrix::Array{Float64, 2}, k::Int64; max_iters::Int64=300
             if verbose
                 println("Successfully terminated with convergence.")
             end
-            
+
             return labels, centroids, sum_squares
-            
+
         elseif iter == max_iters && abs(J - J_previous) > (tol * J)
             throw(error("Failed to converge Check data and/or implementation or increase max_iter."))
         end;
@@ -100,3 +102,17 @@ end
 Kmeans(X, 3)
 
 
+@btime begin
+    num = []
+    ss = []
+    for i = 2:10
+        l, c, s = Kmeans(X, i, verbose=false)
+        push!(num, i)
+        push!(ss, s)
+    end
+end
+
+
+plot(num, ss, ylabel="Sum of Squares", xlabel="Number of Iterations",
+     title = "Test For Heterogeneity Per Iteration", legend=false)
+