26. layers/kmeans

Part: III · Layers
Package: github.com/openfluke/welvet/layers/kmeans
Status: ok — ✅

When

Building or training a net that needs the layers/kmeans Op (also usable as a Parallel cam).

Where

import "github.com/openfluke/welvet/layers/kmeans"

cd 26-kmeans && source ../env.sh && go run .

Why

Soft clustering as a differentiable layer lets topology experiments sit inside the same train loop.

What

Centers on Dense (K×FeatureDim); soft assignment outputs. Full timed matrix + train grids.

Sample output (captured)

[1 4] <nil>

Live capture from go run ./cmd/runall on local ../../welvet (exit 0).

Source

Copied from the Welvet feature book examples (openfluke.github.io/welvet/examples/26-kmeans).

package main

import (
	"fmt"

	"github.com/openfluke/welvet/core"
	"github.com/openfluke/welvet/layers/kmeans"
)

func main() {
	l, err := kmeans.New(kmeans.Config{NumClusters: 4, FeatureDim: 8})
	if err != nil {
		panic(err)
	}
	x := core.NewTensor[float32](1, 8)
	_, y, err := kmeans.Forward(l, x)
	fmt.Println(y.Shape, err)
}