//write data to text file for humans
func printDataToFile(f *os.File, c [][][]float64, total float64, centers [][]float64) {
for k := 0; k < len(c); k++ {
fmt.Println(total)
f.WriteString("CLUSTER:" + strconv.FormatInt(int64(k), 8) + "\n")
f.WriteString("There is " + strconv.FormatFloat(float64(len(c[k]))/total*100.0, 'f', 2, 64) + " percent of the data in this cluster" + "\n")
f.WriteString("The mean of this cluster is " + vectorOperations.ToString(vectorOperations.FindMean(c[k])) + "\n")
f.WriteString("The variance of this cluster is " + vectorOperations.ToString(vectorOperations.FindVariance(c[k], vectorOperations.FindMean(c[k]))) + "\n")
for i := k + 1; i < len(c); i++ {
f.WriteString("The distance between this cluster and cluster " + strconv.FormatInt(int64(i), 8) + " is " +
strconv.FormatFloat(vectorOperations.CalcDistance(centers[i], centers[k]), 'f', 6, 64) + "\n")
}
}
}
//basically feed forward. Give the net a data vector
func (net *Network) compete(vector []float64) (winner int) {
var min float64 = 1 << 63
winner = 0
for i, n := range net.Net[1].Layer {
var sum float64 = 0.0
sum = vectorOperations.CalcDistance(n.Weights, vector)
if sum < min {
min = sum
winner = i
}
}
net.Net[1].Layer[winner].updateWeights(winner, vector)
return
}
//calculate kmeans. k is a parameter as well as the data set to be clustered
//returns the centers and an array with the data vectors in each cluster
func kmeans(numClusters int, matrix [][]float64) (clusters [][][]float64, centers [][]float64) {
r := rand.New(rand.NewSource(10000))
centers = make([][]float64, numClusters)
//make initial random guess at centers
for i := range centers {
centers[i] = make([]float64, len(matrix[0]))
copy(centers[i], matrix[r.Int31n(int32(len(centers)))])
}
var change float64 = 1.0
//main loop. keep interating until there is almost no change in centers.
for change > 0.0001 {
//init some arrays to track rolling sums in clusters and members in each cluster
change = 0.0
counts := make([]int, numClusters)
sums := make([][]float64, numClusters)
clusters = make([][][]float64, numClusters)
for i := range clusters {
clusters[i] = make([][]float64, 0)
}
//make a new arrays for this iteration to sum each attribute in each cluster to find the mean
for i := range sums {
sums[i] = make([]float64, len(matrix[0]))
}
//main clustering logic--loop through each input vector
for i := 0; i < len(matrix); i++ {
bestFit := -1
minDist := 10000.0
//loop through each center and see which center is closer to the current input vector
for j := 0; j < numClusters; j++ {
dist := vectorOperations.CalcDistance(matrix[i], centers[j])
if dist < minDist {
minDist = dist
bestFit = j
}
}
//counts how many vectors are in each cluster
counts[bestFit] += 1
//add this vector's attributes to the rolling sum
for k := range sums[0] {
sums[bestFit][k] += matrix[i][k]
}
//append this vector to the appropriate cluster
clusters[bestFit] = append(clusters[bestFit], matrix[i])
}
//update the new centers as the mean of values in each cluster
for i := range centers {
if counts[i] != 0 {
for j := range centers[i] {
newValue := (sums[i][j] / float64(counts[i]))
change += math.Abs(centers[i][j] - newValue)
centers[i][j] = newValue
}
//if nothing is in this cluster, grab a new vector randomly
//this is really not required unless random vectors are created randomly
} else {
index := r.Int31n(int32(len(centers)))
change += vectorOperations.VectorDiff(centers[i], matrix[index])
copy(centers[i], matrix[index])
}
}
}
return //all done
}
