From eb84a8fe33e3b48c751d23fc0904a48ddb526a90 Mon Sep 17 00:00:00 2001
From: Yoni Nazarathy <yoninazarathy@gmail.com>
Date: Fri, 3 Jul 2020 12:39:23 +1000
Subject: [PATCH] update text

---
 9_chapter/kMeans.jl       | 5 +++--
 9_chapter/kMeansManual.jl | 9 ++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/9_chapter/kMeans.jl b/9_chapter/kMeans.jl
index 68b7272..7d022ee 100644
--- 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
index 76a7de6..ae6fae7 100644
--- 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))