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jerlich · Jul 3, 2020 · eb84a8f · eb84a8f
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diff --git a/9_chapter/kMeans.jl b/9_chapter/kMeans.jl
@@ -1,11 +1,12 @@
 using Clustering, RDatasets, Random, Measures, Plots; pyplot()
 Random.seed!(0)
 
+K = 3
 df = dataset("cluster", "xclara")
 data = copy(convert(Array{Float64}, df)')
 
-seeds = initseeds(:rand, data, 3)
-xclaraKmeans = kmeans(data, 3, init = seeds)
+seeds = initseeds(:rand, data, K)
+xclaraKmeans = kmeans(data, K, init = seeds)
 
 println("Number of clusters: ", nclusters(xclaraKmeans))
 println("Counts of clusters: ", counts(xclaraKmeans))

diff --git a/9_chapter/kMeansManual.jl b/9_chapter/kMeansManual.jl
@@ -1,11 +1,10 @@
 using RDatasets, Distributions, Random
 Random.seed!(0)
 
-k = 3
-
-xclara = dataset("cluster", "xclara")
-n,_ = size(xclara)
-dataPoints = [convert(Array{Float64,1},xclara[i,:]) for i in 1:n]
+K = 3
+df = dataset("cluster", "xclara")
+n,_ = size(df)
+dataPoints = [convert(Array{Float64,1},df[i,:]) for i in 1:n]
 shuffle!(dataPoints)
 
 xMin,xMax = minimum(first.(dataPoints)),maximum(first.(dataPoints))