// chooseCentroids picks random centroids based on the min and max values in the matrix
// and return a k by m matrix of the centroids.
func (c randCentroids) ChooseCentroids(mat *matrix.DenseMatrix, k int) *matrix.DenseMatrix {
_, cols := mat.GetSize()
centroids := matrix.Zeros(k, cols)
for colnum := 0; colnum < cols; colnum++ {
r := mat.ColSlice(colnum)
minj := float64(0)
// min value from column
for _, val := range r {
minj = math.Min(minj, val)
}
// max value from column
maxj := float64(0)
for _, val := range r {
maxj = math.Max(maxj, val)
}
// create a slice of random centroids
// based on maxj + minJ * random num to stay in range
for h := 0; h < k; h++ {
randInRange := ((maxj - minj) * rand.Float64()) + minj
centroids.Set(h, colnum, randInRange)
}
}
return centroids
}
// variance calculates the unbiased variance based on the number of data points
// and centroids (i.e., parameters). In our case, numcentroids should always be 1
// since each data point has been paired with one centroid.
//
// The points matrix contains the coordinates of the data points.
// The centroids matrix is 1Xn that contains the centroid cooordinates.
// variance = // 1 / (numpoints - numcentroids) * sum for all points (x_i - mean_(i))^2
func variance(points, centroid *matrix.DenseMatrix, measurer matutil.VectorMeasurer) (float64, error) {
crows, _ := centroid.GetSize()
if crows > 1 {
return float64(0), errors.New(fmt.Sprintf("variance: expected centroid matrix with 1 row, received matrix with %d rows.", crows))
}
prows, _ := points.GetSize()
// Term 1
t1 := float64(1 / float64((prows - 1)))
// Mean of distance between all points and the centroid.
mean := modelMean(points, centroid)
// Term 2
// Sum over all points (point_i - mean(i))^2
t2 := float64(0)
for i := 0; i < prows; i++ {
p := points.GetRowVector(i)
dist, err := measurer.CalcDist(p, mean)
if err != nil {
return float64(-1), errors.New(fmt.Sprintf("variance: CalcDist returned: %v", err))
}
t2 += math.Pow(dist, 2)
}
variance := t1 * t2
return variance, nil
}
// Xmeans runs k-means for k lower bound to k upper bound on a data set.
// Once the k centroids have converged each cluster is bisected and the BIC
// of the orginal cluster (parent = a model with one centroid) to the
// the bisected model which consists of two centroids and whichever is greater
// is committed to the set of clusters for this larger model k.
//
func Xmeans(datapoints, centroids *matrix.DenseMatrix, kmax int, cc, bisectcc CentroidChooser, measurer VectorMeasurer) ([]Model, map[string]error) {
logname := "/var/tmp/xmeans.log"
fp, err := os.OpenFile(logname, os.O_RDWR|os.O_APPEND, 0666)
if err != nil {
if os.IsNotExist(err) {
fp, err = os.Create(logname)
if err != nil {
fmt.Printf("Xmeans: cannot open %s for logging.\n", logname)
}
}
}
log.SetOutput(io.Writer(fp))
k, _ := centroids.GetSize()
log.Printf("Start k=%d kmax=%d\n", k, kmax)
R, M := datapoints.GetSize()
errs := make(map[string]error)
runtime.GOMAXPROCS(numworkers)
models := make([]Model, 0)
for k <= kmax {
log.Printf("kmeans started k=%d\n", k)
model, err := kmeans(datapoints, centroids, measurer)
if err != nil {
errs[strconv.Itoa(k)] = err
}
// Bisect the returned clusters
log.Println("bisect started")
bimodel := bisect(model.Clusters, R, M, bisectcc, measurer)
numCentroids := len(bimodel.Clusters)
log.Printf("bisect returned %d clusters\n", numCentroids)
models = append(models, model)
var cent *matrix.DenseMatrix
if numCentroids <= kmax {
for rowexists := true; rowexists == true; {
cent = cc.ChooseCentroids(datapoints, 1)
rowexists = centroids.RowExists(cent)
}
centroids, err = centroids.AppendRow(cent)
if err != nil {
log.Printf("AppendRow: %v\n", err)
errs["ApppendRow"] = err
break
}
k++
} else {
k = numCentroids
}
}
log.Println("Finished")
return models, errs
}
// ComputeCentroids Needs comments.
func ComputeCentroid(mat *matrix.DenseMatrix) (*matrix.DenseMatrix, error) {
rows, _ := mat.GetSize()
vectorSum := mat.SumCols()
if rows == 0 {
return vectorSum, errors.New("No points inputted")
}
vectorSum.Scale(1.0 / float64(rows))
return vectorSum, nil
}
// addPairPointCentroidJobs adds a job to the jobs channel.
func addPairPointCentroidJobs(jobs chan<- PairPointCentroidJob, datapoints,
centroids *matrix.DenseMatrix, measurer VectorMeasurer, results chan<- PairPointCentroidResult) {
numRows, _ := datapoints.GetSize()
for i := 0; i < numRows; i++ {
point := datapoints.GetRowVector(i)
jobs <- PairPointCentroidJob{point, centroids, results, i, measurer}
}
close(jobs)
}
// Kmeansp returns means and distance squared of the coordinates for each
// centroid using parallel computation.
//
// Input values
//
// datapoints - a kX2 matrix of R^2 coordinates
//
// centroids - a kX2 matrix of R^2 coordinates for centroids.
//
// measurer - anythng that implements the matutil.VectorMeasurer interface to
// calculate the distance between a centroid and datapoint. (e.g., Euclidian
// distance)
//
// Return values
//
// centroidMean - a kX2 matrix where the row number corresponds to the same
// row in the centroid matrix and the two columns are the means of the
// coordinates for that cluster. i.e., the best centroids that could
// be determined.
//
// ____ ______
// | 12.29 32.94 | <-- The mean of coordinates for centroid 0
// | 4.6 29.22 | <-- The mean of coordinates for centroid 1
// |_____ ______|
//
//
// centroidSqErr - a kX2 matrix where the first column contains a number
// indicating the centroid and the second column contains the minimum
// distance between centroid and point squared. (i.e., the squared error)
//
// ____ _______
// | 0 38.01 | <-- Centroid 0, squared error for the coordinates in row 0 of datapoints
// | 1 23 .21| <-- Centroid 1, squared error for the coordinates in row 1 of datapoints
// | 0 14.12 | <-- Centroid 0, squared error for the coordinates in row 2 of datapoints
// _____ _______
//func Kmeansp(datapoints, centroids *matrix.DenseMatrix, measurer matutil.VectorMeasurer) (centroidMean,
func Kmeansp(datapoints *matrix.DenseMatrix, k int, cc CentroidChooser, measurer matutil.VectorMeasurer) (centroidMean,
centroidSqErr *matrix.DenseMatrix, err error) {
//k, _ := centroids.GetSize()
fp, _ := os.Create("/var/tmp/km.log")
w := io.Writer(fp)
log.SetOutput(w)
centroids := cc.ChooseCentroids(datapoints, k)
numRows, numCols := datapoints.GetSize()
centroidSqErr = matrix.Zeros(numRows, numCols)
centroidMean = matrix.Zeros(k, numCols)
jobs := make(chan PairPointCentroidJob, numworkers)
results := make(chan PairPointCentroidResult, minimum(1024, numRows))
done := make(chan int, numworkers)
go addPairPointCentroidJobs(jobs, datapoints, centroidSqErr, centroids, measurer, results)
for i := 0; i < numworkers; i++ {
go doPairPointCentroidJobs(done, jobs)
}
go awaitPairPointCentroidCompletion(done, results)
processPairPointToCentroidResults(centroidSqErr, results) // This blocks so that all the results can be processed
// Now that you have each data point grouped with a centroid, iterate
// through the centroidSqErr martix and for each centroid retrieve the
// original coordinates from datapoints and place the results in
// pointsInCuster.
for c := 0; c < k; c++ {
// c is the index that identifies the current centroid.
// d is the index that identifies a row in centroidSqErr and datapoints.
// Select all the rows in centroidSqErr whose first col value == c.
// Get the corresponding row vector from datapoints and place it in pointsInCluster.
matches, err := centroidSqErr.FiltColMap(float64(c), float64(c), 0) //rows with c in column 0.
if err != nil {
return centroidMean, centroidSqErr, nil
}
// It is possible that some centroids will not have any points, so there
// may not be any matches in the first column of centroidSqErr.
if len(matches) == 0 {
continue
}
pointsInCluster := matrix.Zeros(len(matches), 2)
for d, rownum := range matches {
pointsInCluster.Set(d, 0, datapoints.Get(int(rownum), 0))
pointsInCluster.Set(d, 1, datapoints.Get(int(rownum), 1))
}
// pointsInCluster now contains all the data points for the current
// centroid. Take the mean of each of the 2 cols in pointsInCluster.
means := pointsInCluster.MeanCols()
centroidMean.Set(c, 0, means.Get(0, 0))
centroidMean.Set(c, 1, means.Get(0, 1))
}
return
}
// modelMean calculates the mean between all points in a model and a centroid.
func modelMean(points, centroid *matrix.DenseMatrix) *matrix.DenseMatrix {
prows, pcols := points.GetSize()
pdist := matrix.Zeros(prows, pcols)
for i := 0; i < prows; i++ {
diff := matrix.Difference(centroid, points.GetRowVector(i))
pdist.SetRowVector(diff, i)
}
return pdist.MeanCols()
}
// CalcDist finds the ManhattanDistance which is the sum of the aboslute
// difference of the coordinates. Also known as rectilinear distance,
// city block distance, or taxicab distance.
func (md ManhattanDist) CalcDist(a, b *matrix.DenseMatrix) (dist float64, err error) {
dist = float64(0)
err = nil
arows, acols := a.GetSize()
brows, bcols := b.GetSize()
if arows != 1 || brows != 1 {
return dist, errors.New(fmt.Sprintf("matutil: Matrices must contain only 1 row. a has %d and b has %d.", arows, brows))
} else if arows != brows {
return dist, errors.New(fmt.Sprintf("matutil: Matrices must have the same dimensions. a=%dX%d b=%dX%d", arows, acols, brows, bcols))
}
dist = math.Abs(a.Get(0, 0)-b.Get(0, 0)) + math.Abs(a.Get(0, 1)-b.Get(0, 1))
return
}
// Needs comments
func (c EllipseCentroids) ChooseCentroids(mat *matrix.DenseMatrix, k int) *matrix.DenseMatrix {
_, cols := mat.GetSize()
var xmin, xmax, ymin, ymax = matutil.GetBoundaries(mat)
x0, y0 := xmin+(xmax-xmin)/2.0, ymin+(ymax-ymin)/2.0
centroids := matrix.Zeros(k, cols)
rx, ry := xmax-x0, ymax-y0
thetaInit := rand.Float64() * math.Pi
for i := 0; i < k; i++ {
centroids.Set(i, 0, rx*c.frac*math.Cos(thetaInit+float64(i)*math.Pi/float64(k)))
centroids.Set(i, 1, ry*c.frac*math.Sin(thetaInit+float64(i)*math.Pi/float64(k)))
}
return centroids
}
// EllipseCentroids lays out the initial centroids evenly along an elipse inscribed and centered within the boundaries of the dataset.
// It is only defined for M=2
// * Frac: This must be a float between 0 and 1. It determines the scale of the inscribing ellipse relative to the dataset,
// so Frac==1.0 produces an ellipse that spans the entire dataset, while Frac==0.5 produces an ellipse spanning half the dataset.
func (c EllipseCentroids) ChooseCentroids(mat *matrix.DenseMatrix, k int) *matrix.DenseMatrix {
_, cols := mat.GetSize()
// TODO Cache boundaries call for each matrix so that it is not called on each bisect
var xmin, xmax, ymin, ymax = boundaries(mat)
x0, y0 := xmin+(xmax-xmin)/2.0, ymin+(ymax-ymin)/2.0
centroids := matrix.Zeros(k, cols)
rx, ry := xmax-x0, ymax-y0
thetaInit := rand.Float64() * math.Pi
for i := 0; i < k; i++ {
centroids.Set(i, 0, rx*c.Frac*math.Cos(thetaInit+float64(i)*2.0*math.Pi/float64(k)))
centroids.Set(i, 1, ry*c.Frac*math.Sin(thetaInit+float64(i)*2.0*math.Pi/float64(k)))
}
return centroids
}
// DataCentroids picks k distinct points from the dataset. If k is > points in
// the matrix then k is set to the number of points.
func (c DataCentroids) ChooseCentroids(mat *matrix.DenseMatrix, k int) *matrix.DenseMatrix {
// first set up a map to keep track of which data points have already been chosen so we don't dupe
rows, cols := mat.GetSize()
centroids := matrix.Zeros(k, cols)
if k > rows {
k = rows
}
chosenIdxs := make(map[int]bool, k)
for len(chosenIdxs) < k {
index := rand.Intn(rows)
chosenIdxs[index] = true
}
i := 0
for idx, _ := range chosenIdxs {
centroids.SetRowVector(mat.GetRowVector(idx).Copy(), i)
i += 1
}
return centroids
}
// boundaries returns the max and min x and y values for a dense matrix
// of shape m x m.
func boundaries(mat *matrix.DenseMatrix) (xmin, xmax, ymin, ymax float64) {
rows, _ := mat.GetSize()
xmin, ymin = mat.Get(0, 0), mat.Get(0, 1)
xmax, ymax = mat.Get(0, 0), mat.Get(0, 1)
for i := 1; i < rows; i++ {
xi, yi := mat.Get(i, 0), mat.Get(i, 1)
if xi > xmax {
xmax = xi
} else if xi < xmin {
xmin = xi
}
if yi > ymax {
ymax = yi
} else if yi < ymin {
ymin = yi
}
}
return
}
func makeCentPointDist(datapoints, centroids *matrix.DenseMatrix) *matrix.DenseMatrix {
r, c := datapoints.GetSize()
CentPointDist := matrix.Zeros(r, c)
done := make(chan int)
jobs := make(chan PairPointCentroidJob, r)
results := make(chan PairPointCentroidResult, minimum(1024, r))
var ed EuclidDist
go addPairPointCentroidJobs(jobs, datapoints, centroids, ed, results)
for i := 0; i < r; i++ {
go doPairPointCentroidJobs(done, jobs)
}
go awaitPairPointCentroidCompletion(done, results)
clusterChanged := assessClusters(CentPointDist, results)
if clusterChanged == true || clusterChanged == false {
}
return CentPointDist
}
// GetBoundaries returns the max and min x and y values for a dense matrix
// of shape m x 2.
func GetBoundaries(mat *matrix.DenseMatrix) (xmin, xmax, ymin, ymax float64) {
rows, cols := mat.GetSize()
if cols != 2 {
// TODO - should there be an err return, or should we panic here?
}
xmin, ymin = mat.Get(0, 0), mat.Get(0, 1)
xmax, ymax = mat.Get(0, 0), mat.Get(0, 1)
for i := 1; i < rows; i++ {
xi, yi := mat.Get(i, 0), mat.Get(i, 1)
if xi > xmax {
xmax = xi
} else if xi < xmin {
xmin = xi
}
if yi > ymax {
ymax = yi
} else if yi < ymin {
ymin = yi
}
}
return
}
// kmeans partitions datapoints into K clusters. This results in a partitioning of
// the data space into Voronoi cells. The problem is NP-hard so here we attempt
// to parallelize or make concurrent as many processes as possible to reduce the
// running time.
//
// 1. Place K points into the space represented by the objects that are being clustered.
// These points represent initial group centroids.
//
// 2. Assign each object to the group that has the closest centroid.
//
// 3. When all objects have been assigned, recalculate the positions of the K centroids
// by calculating the mean of all cooridnates in a cluster and making that
// the new centroid.
//
// 4. Repeat Steps 2 and 3 until the centroids no longer move.
//
// centroids is K x M matrix that cotains the coordinates for the centroids.
// The centroids are indexed by the 0 based rows of this matrix.
// ____ _________
// | 12.29 32.94 ... | <-- The coordinates for centroid 0
// | 4.6 29.22 ... | <-- The coordinates for centroid 1
// |_____ __________|
//
//
// CentPointDist is ax R x M matrix. The rows have a 1:1 relationship to
// the rows in datapoints. Column 0 contains the row number in centroids
// that corresponds to the centroid for the datapoint in row i of this matrix.
// Column 1 contains (x_i - mu(i))^2.
// ____ _______
// | 3 38.01 | <-- Centroid 3, squared error for the coordinates in row 0 of datapoints
// | 1 23 .21| <-- Centroid 1, squared error for the coordinates in row 1 of datapoints
// | 0 14.12 | <-- Centroid 0, squared error for the coordinates in row 2 of datapoints
// _____ _______
//
func kmeans(datapoints, centroids *matrix.DenseMatrix, measurer VectorMeasurer) Model {
/* datapoints CentPoinDist centroids
________________
____ ____ __|__ ______ | ____ ___________
| ... | | ... | V | ... |
| 3.0 5.1| <-- row i --> | 3 32.12 | row 3 | 3 38.1, ... |
|____ ___| |____ ______| |___ __________ |
*/
R, M := datapoints.GetSize()
CentPointDist := matrix.Zeros(R, 2)
k, _ := centroids.GetSize()
clusterChanged := true
var clusters []cluster
for clusterChanged == true {
clusterChanged = false
clusters = make([]cluster, 0)
jobs := make(chan PairPointCentroidJob, 1024)
results := make(chan PairPointCentroidResult, 1024)
done := make(chan int, 1024)
// Pair each point with its closest centroid.
go addPairPointCentroidJobs(jobs, datapoints, centroids, measurer, results)
for i := 0; i < numworkers; i++ {
go doPairPointCentroidJobs(done, jobs)
}
go awaitPairPointCentroidCompletion(done, results)
clusterChanged = assessClusters(CentPointDist, results) // This blocks so that all the results can be processed
// You have each data point grouped with a centroid,
for idx, cent := 0, 0; cent < k; cent++ {
// Select all the rows in CentPointDist whose first col value == cent.
// Get the corresponding row vector from datapoints and place it in pointsInCluster.
r, _ := CentPointDist.GetSize()
matches := make([]int, 0)
for i := 0; i < r; i++ {
v := CentPointDist.Get(i, 0)
if v == float64(cent) {
matches = append(matches, i)
}
}
// It is possible that some centroids may have zero points, so there
// may not be any matches.
if len(matches) == 0 {
continue
}
pointsInCluster := matrix.Zeros(len(matches), M)
i := 0
for _, rownum := range matches {
pointsInCluster.Set(i, 0, datapoints.Get(int(rownum), 0))
pointsInCluster.Set(i, 1, datapoints.Get(int(rownum), 1))
i++
}
// pointsInCluster now contains all the data points for the current
// centroid. The mean of the coordinates for this cluster becomes
// the new centroid for this cluster.
mean := pointsInCluster.MeanCols()
centroids.SetRowVector(mean, cent)
clust := cluster{pointsInCluster, mean, 0}
clust.Variance = variance(clust, measurer)
clusters = append(clusters, clust)
idx++
}
}
modelbic := calcbic(R, M, clusters)
model := Model{modelbic, clusters}
return model
}
// Kmeansbi bisects a given cluster and determines which centroids give the lowest error.
// Take the points in a cluster
// While the number of cluster < k
// for every cluster
// measure total error
// cacl kmeansp with k=2 on a given cluster
// measure total error after kmeansp split
// choose the cluster split with the lowest SSE
// commit the chosen split
//
// N.B. We are using SSE until the BIC is completed.
func Kmeansbi(datapoints *matrix.DenseMatrix, k int, cc CentroidChooser, measurer matutil.VectorMeasurer) (matCentroidlist, clusterAssignment *matrix.DenseMatrix, err error) {
numRows, numCols := datapoints.GetSize()
clusterAssignment = matrix.Zeros(numRows, numCols)
matCentroidlist = matrix.Zeros(k, numCols)
centroid0 := datapoints.MeanCols()
centroidlist := []*matrix.DenseMatrix{centroid0}
// Initially create one cluster.
for j := 0; j < numRows; j++ {
point := datapoints.GetRowVector(j)
distJ, err := measurer.CalcDist(centroid0, point)
if err != nil {
return matCentroidlist, clusterAssignment, errors.New(fmt.Sprintf("Kmeansbi: CalcDist returned err=%v", err))
}
clusterAssignment.Set(j, 1, math.Pow(distJ, 2))
}
var bestClusterAssignment, bestNewCentroids *matrix.DenseMatrix
var bestCentroidToSplit int
// Find the best centroid configuration.
for len(centroidlist) < k {
lowestSSE := math.Inf(1)
// Split cluster
for i, _ := range centroidlist {
// Get the points in this cluster
pointsCurCluster, err := clusterAssignment.FiltCol(float64(i), float64(i), 0)
if err != nil {
return matCentroidlist, clusterAssignment, err
}
centroids, splitClusterAssignment, err := Kmeansp(pointsCurCluster, 2, cc, measurer)
if err != nil {
return matCentroidlist, clusterAssignment, err
}
/* centroids is a 2X2 matrix of the best centroids found by kmeans
splitClustAssignment is a mX2 matrix where col0 is either 0 or 1 and refers to the rows in centroids
where col1 cotains the squared error between a centroid and a point. The rows here correspond to
the rows in ptsInCurrCluster. For example, if row 2 contains [1, 7.999] this means that centroid 1
has been paired with the point in row 2 of splitClustAssignment and that the squared error (distance
between centroid and point) is 7.999.
*/
// Calculate the sum of squared errors for each centroid.
// This give a statistcal measurement of how good
// the clustering is for this cluster.
sseSplit := splitClusterAssignment.SumCol(1)
// Calculate the SSE for the original cluster
sqerr, err := clusterAssignment.FiltCol(float64(0), math.Inf(1), 0)
if err != nil {
return matCentroidlist, clusterAssignment, err
}
sseNotSplit := sqerr.SumCol(1)
// TODO: Pre-BCI is this the best way to evaluate?
if sseSplit+sseNotSplit < lowestSSE {
bestCentroidToSplit = 1
bestNewCentroids = matrix.MakeDenseCopy(centroids)
bestClusterAssignment = matrix.MakeDenseCopy(splitClusterAssignment)
}
}
// Applying the split overwrites the existing cluster assginments for the
// cluster you have decided to split. Kmeansp() returned two clusters
// labeled 0 and 1. Change these cluster numbers to the cluster number
// you are splitting and the next cluster to be added.
m, err := bestClusterAssignment.FiltColMap(1, 1, 0)
if err != nil {
return matCentroidlist, clusterAssignment, err
}
for i, _ := range m {
bestClusterAssignment.Set(i, 0, float64(len(centroidlist)))
}
n, err := bestClusterAssignment.FiltColMap(0, 0, 0)
if err != nil {
return matCentroidlist, clusterAssignment, err
}
for i, _ := range n {
bestClusterAssignment.Set(i, 1, float64(bestCentroidToSplit))
}
fmt.Printf("Best centroid to split %f\n", bestCentroidToSplit)
r, _ := bestClusterAssignment.GetSize()
fmt.Printf("The length of best cluster assesment is %f\n", r)
// Replace a centroid with the two best centroids from the split.
centroidlist[bestCentroidToSplit] = bestNewCentroids.GetRowVector(0)
centroidlist = append(centroidlist, bestNewCentroids.GetRowVector(1))
// Reassign new clusters and SSE
rows, _ := clusterAssignment.GetSize()
for i, j := 0, 0; i < rows; i++ {
if clusterAssignment.Get(i, 0) == float64(bestCentroidToSplit) {
clusterAssignment.Set(i, 0, bestClusterAssignment.Get(j, 0))
clusterAssignment.Set(i, 1, bestClusterAssignment.Get(j, 1))
j++
}
}
// make centroidlist into a matrix
s := make([][]float64, len(centroidlist))
for i, mat := range centroidlist {
s[i][0] = mat.Get(0, 0)
s[i][1] = mat.Get(0, 1)
}
matCentroidlist = matrix.MakeDenseMatrixStacked(s)
}
return matCentroidlist, clusterAssignment, nil
}
// Xmeans runs k-means for k lower bound to k upper bound on a data set.
// Once the k centroids have converged each cluster is bisected and the BIC
// of the orginal cluster (parent = a model with one centroid) to the
// the bisected model which consists of two centroids and whichever is greater
// is committed to the set of clusters for this larger model k.
//
func Xmeans(datapoints, centroids *matrix.DenseMatrix, k, kmax int, cc, bisectcc CentroidChooser, measurer VectorMeasurer) ([]Model, map[string]error) {
var err error
// Uncomment logging code as well as the import statement above if you want simple logging to the elapsed
// time between major events.
/* logname := "/var/tmp/xmeans.log"
fp, err := os.OpenFile(logname, os.O_RDWR|os.O_APPEND, 0666)
if err != nil {
if os.IsNotExist(err) {
fp, err = os.Create(logname)
if err != nil {
fmt.Printf("Xmeans: cannot open %s for logging.\n", logname)
}
}
}
log.SetOutput(io.Writer(fp))
*/
if k > kmax {
m := make([]Model, 0)
e := map[string]error{
"k": errors.New(fmt.Sprintf("k must be <= kmax. Received k=%d and kmax=%d.", k, kmax)),
}
return m, e
}
// log.Printf("Start k=%d kmax=%d\n", k, kmax)
R, M := datapoints.GetSize()
errs := make(map[string]error)
runtime.GOMAXPROCS(numworkers)
models := make([]Model, 0)
for k <= kmax {
// log.Printf("kmeans started k=%d\n", k)
model := kmeans(datapoints, centroids, measurer)
// Bisect the returned clusters
// log.Println("bisect started")
bimodel := bisect(model.Clusters, R, M, bisectcc, measurer)
numCentroids := len(bimodel.Clusters)
// log.Printf("bisect returned %d clusters\n", numCentroids)
models = append(models, model)
var cent *matrix.DenseMatrix
if numCentroids <= kmax {
for rowexists := true; rowexists == true; {
cent = cc.ChooseCentroids(datapoints, 1)
rowexists = centroids.RowExists(cent)
}
centroids, err = centroids.AppendRow(cent)
if err != nil {
errs["ApppendRow"] = err
break
}
k++
} else {
k = numCentroids
}
}
// log.Println("Finished")
return models, errs
}