68 lines
1.4 KiB
Go
68 lines
1.4 KiB
Go
package object
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import (
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"fmt"
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"strconv"
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"strings"
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"github.com/muesli/clusters"
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"github.com/muesli/kmeans"
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"github.com/openbrainorg/openbrain/util"
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)
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func fa2Str(floatArray []float64) string {
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sData := []string{}
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for _, f := range floatArray {
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sData = append(sData, fmt.Sprintf("%f", f))
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}
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return strings.Join(sData, "|")
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}
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func runKmeans(vectors []*Vector, clusterNumber int) {
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vectorMap := map[string]*Vector{}
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var d clusters.Observations
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for _, vector := range vectors {
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if len(vector.Data) == 0 {
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continue
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}
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dataKey := vector.GetDataKey()
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vectorMap[dataKey] = vector
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d = append(d, clusters.Coordinates(vector.Data))
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}
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km := kmeans.New()
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cs, err := km.Partition(d, clusterNumber)
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if err != nil {
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panic(err)
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}
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for i, c := range cs {
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fmt.Printf("Centered at x: %.2f y: %.2f\n", c.Center[0], c.Center[1])
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fmt.Printf("Matching data points: %+v\n\n", c.Observations)
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color := util.GetRandomColor()
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for _, observation := range c.Observations {
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floatArray := observation.Coordinates()
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dataKey := fa2Str(floatArray)
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vector, ok := vectorMap[dataKey]
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if !ok {
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panic(fmt.Errorf("vectorMap vector not found, dataKey = %s", dataKey))
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}
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vector.Category = strconv.Itoa(i)
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vector.Color = color
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}
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}
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}
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func updateWordsetVectorCategories(owner string, wordsetName string) {
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wordset := getWordset(owner, wordsetName)
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runKmeans(wordset.Vectors, 100)
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UpdateWordset(wordset.GetId(), wordset)
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}
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