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Adding dep files and dependencies.

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gokmeans
======
A Go (golang) package that implements the K-means clustering algorithm.
Goroutines are used throughout to do some calulations concurrently.
#### Get package
`go get github.com/mdesenfants/gokmeans`
#### Example
```
package main
import (
"fmt"
"github.com/mdesenfants/gokmeans"
)
var observations []gokmeans.Node = []gokmeans.Node {
gokmeans.Node{20.0, 20.0, 20.0, 20.0},
gokmeans.Node{21.0, 21.0, 21.0, 21.0},
gokmeans.Node{100.5, 100.5, 100.5, 100.5},
gokmeans.Node{50.1, 50.1, 50.1, 50.1},
gokmeans.Node{64.2, 64.2, 64.2, 64.2},
}
func main() {
// Get a list of centroids and output the values
if success, centroids := gokmeans.Train(observations, 2, 50); success {
// Show the centroids
fmt.Println("The centroids are")
for _, centroid := range centroids {
fmt.Println(centroid)
}
// Output the clusters
fmt.Println("...")
for _, observation := range observations {
index := gokmeans.Nearest(observation, centroids)
fmt.Println(observation, "belongs in cluster", index+1, ".")
}
}
}
```

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package gokmeans
import (
"math/rand"
"time"
)
type Node []float64
func Train(Nodes []Node, clusterCount int, maxRounds int) (bool, []Node) {
if int(len(Nodes)) < clusterCount {
return false, nil
}
// Check to make sure everything is consistent, dimension-wise
stdLen := 0
for i, Node := range Nodes {
curLen := len(Node)
if i > 0 && len(Node) != stdLen {
return false, nil
}
stdLen = curLen
}
centroids := make([]Node, clusterCount)
r := rand.New(rand.NewSource(time.Now().UnixNano()))
// Pick centroid starting points from Nodes
for i := 0; i < clusterCount; i++ {
srcIndex := r.Intn(len(Nodes))
srcLen := len(Nodes[srcIndex])
centroids[i] = make(Node, srcLen)
copy(centroids[i], Nodes[r.Intn(len(Nodes))])
}
// Train centroids
movement := true
for i := 0; i < maxRounds && movement; i++ {
movement = false
groups := make(map[int][]Node)
for _, Node := range Nodes {
near := Nearest(Node, centroids)
groups[near] = append(groups[near], Node)
}
for key, group := range groups {
newNode := meanNode(group)
if !equal(centroids[key], newNode) {
centroids[key] = newNode
movement = true
}
}
}
return true, centroids
}
func equal(node1, node2 Node) bool {
if len(node1) != len(node2) {
return false
}
for i, v := range node1 {
if v != node2[i] {
return false
}
}
return true
}
func Nearest(in Node, nodes []Node) int {
count := len(nodes)
results := make(Node, count)
cnt := make(chan int)
for i, node := range nodes {
go func(i int, node, cl Node) {
results[i] = distance(in, node)
cnt <- 1
}(i, node, in)
}
wait(cnt, results)
mindex := 0
curdist := results[0]
for i, dist := range results {
if dist < curdist {
curdist = dist
mindex = i
}
}
return mindex
}
func distance(node1 Node, node2 Node) float64 {
length := len(node1)
squares := make(Node, length, length)
cnt := make(chan int)
for i, _ := range node1 {
go func(i int) {
diff := node1[i] - node2[i]
squares[i] = diff * diff
cnt <- 1
}(i)
}
wait(cnt, squares)
sum := 0.0
for _, val := range squares {
sum += val
}
return sum
}
func meanNode(values []Node) Node {
newNode := make(Node, len(values[0]))
for _, value := range values {
for j := 0; j < len(newNode); j++ {
newNode[j] += value[j]
}
}
for i, value := range newNode {
newNode[i] = value / float64(len(values))
}
return newNode
}
func wait(c chan int, values Node) {
count := len(values)
<-c
for respCnt := 1; respCnt < count; respCnt++ {
<-c
}
}