milvus/internal/util/clustering/clustering.go

51 lines
1.3 KiB
Go

package clustering
import (
"encoding/binary"
"math"
"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
"github.com/milvus-io/milvus/pkg/util/distance"
"github.com/milvus-io/milvus/pkg/util/merr"
)
func CalcVectorDistance(dim int64, dataType schemapb.DataType, left []byte, right []float32, metric string) ([]float32, error) {
switch dataType {
case schemapb.DataType_FloatVector:
distance, err := distance.CalcFloatDistance(dim, DeserializeFloatVector(left), right, metric)
if err != nil {
return nil, err
}
return distance, nil
// todo support other vector type
case schemapb.DataType_BinaryVector:
case schemapb.DataType_Float16Vector:
case schemapb.DataType_BFloat16Vector:
default:
return nil, merr.ErrParameterInvalid
}
return nil, nil
}
func DeserializeFloatVector(data []byte) []float32 {
vectorLen := len(data) / 4 // Each float32 occupies 4 bytes
fv := make([]float32, vectorLen)
for i := 0; i < vectorLen; i++ {
bits := binary.LittleEndian.Uint32(data[i*4 : (i+1)*4])
fv[i] = math.Float32frombits(bits)
}
return fv
}
func SerializeFloatVector(fv []float32) []byte {
data := make([]byte, 0, 4*len(fv)) // float32 occupies 4 bytes
buf := make([]byte, 4)
for _, f := range fv {
binary.LittleEndian.PutUint32(buf, math.Float32bits(f))
data = append(data, buf...)
}
return data
}