Use random data points in the simple example and README

README.md

@@ -15,25 +15,22 @@ to the cluster with the nearest mean, serving as a prototype of the cluster.
 ```go
 import "github.com/muesli/kmeans"
 
-km := kmeans.New()
+// set up a random two-dimensional data set (float64 values between 0.0 and 1.0)
-
-// set up "random" data points (float64 values between 0.0 and 1.0)
 var d kmeans.Points
-for x := 0; x < 255; x += 4 {
+for x := 0; x < 1024; x++ {
-	for y := 0; y < 255; y += 4 {
+	d = append(d, kmeans.Point{
-		d = append(d, kmeans.Point{
+		rand.Float64(),
-			float64(x) / 255.0,
+		rand.Float64(),
-			float64(y) / 255.0,
+	})
-		})
-	}
 }
 
 // Partition the data points into 16 clusters
+km := kmeans.New()
 clusters, err := km.Partition(d, 16)
 
 for _, c := range clusters {
 	fmt.Printf("Centered at x: %.2f y: %.2f\n", c.Center[0]*255.0, c.Center[1]*255.0)
-	fmt.Printf("Points: %+v\n\n", c.Points)
+	fmt.Printf("Matching data points: %+v\n\n", c.Points)
 }
 ```
 

examples/simple/main.go

@@ -2,26 +2,28 @@ package main
 
 import (
 	"fmt"
+	"math/rand"
+	"time"
 
 	"github.com/muesli/kmeans"
 )
 
 func main() {
-	// set up "random" data points (float64 values between 0.0 and 1.0)
+	rand.Seed(time.Now().UnixNano())
-	km := kmeans.New()
+
+	// set up a random two-dimensional data set (float64 values between 0.0 and 1.0)
 	var d kmeans.Points
-	for x := 0; x < 255; x += 4 {
+	for x := 0; x < 1024; x++ {
-		for y := 0; y < 255; y += 4 {
+		d = append(d, kmeans.Point{
-			d = append(d, kmeans.Point{
+			rand.Float64(),
-				float64(x) / 255.0,
+			rand.Float64(),
-				float64(y) / 255.0,
+		})
-			})
-		}
 	}
 	fmt.Printf("%d data points\n", len(d))
 
-	// Partition the data points into 4 clusters
+	// Partition the data points into 7 clusters
-	clusters, _ := km.Partition(d, 4)
+	km := kmeans.New()
+	clusters, _ := km.Partition(d, 7)
 
 	for i, c := range clusters {
 		fmt.Printf("Cluster: %d\n", i)