Documentation
¶
Overview ¶
Package meanshift provides mean shift clustering for ℝⁿ data.
Example ¶
package main
import (
"github.com/biogo/cluster/meanshift"
"fmt"
"strings"
)
type Feature struct {
ID string
Start int
End int
}
func (f *Feature) Len() int { return f.End - f.Start }
type Features []*Feature
func (f Features) Len() int { return len(f) }
func (f Features) Values(i int) []float64 { return []float64{float64(f[i].Start), float64(f[i].End)} }
var feats = []*Feature{
{ID: "0", Start: 1, End: 1700},
{ID: "1", Start: 2, End: 1700},
{ID: "2", Start: 3, End: 610},
{ID: "3", Start: 2, End: 605},
{ID: "4", Start: 1, End: 600},
{ID: "5", Start: 2, End: 750},
{ID: "6", Start: 650, End: 900},
{ID: "7", Start: 700, End: 950},
{ID: "8", Start: 1000, End: 1700},
{ID: "9", Start: 950, End: 1712},
{ID: "10", Start: 1000, End: 1650},
}
func main() {
ms := meanshift.New(Features(feats), meanshift.NewTruncGauss(60, 3), 0.1, 5)
err := ms.Cluster()
if err != nil {
fmt.Println(err)
return
}
for ci, c := range ms.Centers() {
fmt.Printf("Cluster %d:\n", ci)
for _, i := range c.Members() {
f := feats[i]
fmt.Printf("%2s %s%s\n",
f.ID,
strings.Repeat(" ", f.Start/20),
strings.Repeat("-", f.Len()/20),
)
}
fmt.Println()
}
var within float64
for _, ss := range ms.Within() {
within += ss
}
fmt.Printf("betweenSS / totalSS = %.6f\n", 1-(within/ms.Total()))
}
Output: Cluster 0: 5 ------------------------------------- Cluster 1: 0 ------------------------------------------------------------------------------------ 1 ------------------------------------------------------------------------------------ Cluster 2: 4 ----------------------------- 3 ------------------------------ 2 ------------------------------ Cluster 3: 7 ------------ 6 ------------ Cluster 4: 9 -------------------------------------- 8 ----------------------------------- 10 -------------------------------- betweenSS / totalSS = 0.998655
Index ¶
Examples ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type MeanShift ¶
type MeanShift struct {
// contains filtered or unexported fields
}
MeanShift implements data clustering using the mean shift algorithm.
func New ¶
New creates a new mean shift Clusterer object populated with data from an Interface value, data and using the Shifter k.
func (*MeanShift) Total ¶
Total calculates the total sum of squares for the data relative to the data mean.
type Shifter ¶
type Shifter interface {
// Init initialises the Shifter with the provided data.
Init(cluster.Interface)
// Shift performs a single iteration of the mean shift algorithm and
// returns the sum of squares differences between the initial state
// and the final state.
Shift() float64
// Bandwidth returns the bandwidth parameter of the Shifter.
Bandwidth() float64
// Centers returns the cluster centers of the clustered data.
Centers() []cluster.Center
}
Shifter implements a single step of the mean shift algorithm.
type TruncGauss ¶
type TruncGauss struct {
// contains filtered or unexported fields
}
func NewTruncGauss ¶
func NewTruncGauss(h, oversample float64) *TruncGauss
func (*TruncGauss) Bandwidth ¶
func (s *TruncGauss) Bandwidth() float64
func (*TruncGauss) Centers ¶
func (s *TruncGauss) Centers() []cluster.Center
func (*TruncGauss) Init ¶
func (s *TruncGauss) Init(data cluster.Interface)
func (*TruncGauss) Shift ¶
func (s *TruncGauss) Shift() (delta float64)
Source Files
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