kmeans

README

kmeans

kmeans package is a Go implementation of k-means clustering algorithm with both BLAS and naive implementations.

Documentation

Index

• Constants
• Variables
• type KMeans
  • func LoadKMeans(dec *gob.Decoder) (*KMeans, error)
  • func NewKMeans(numClusters, numFeatures int, opts ...NewOption) (*KMeans, error)
  • func (km *KMeans) Centroids() [][]float32
  • func (km *KMeans) Decode(dec *gob.Decoder) error
  • func (km *KMeans) Encode(enc *gob.Encoder) error
  • func (km *KMeans) Predict(data []float32, fn func(row, minCol int, minVal float32) error) error
  • func (km *KMeans) Train(data []float32, opts ...TrainOption) (int, float32, error)
• type KMeansState
• type NewOption
  • func WithInitMethod(method int) NewOption
• type TrainOption
  • func WithConcurrency(concurrency int) TrainOption
  • func WithMaxIterations(iter int) TrainOption
  • func WithTolerance(tol float32) TrainOption

Constants

const (
	INIT_NONE = iota
	INIT_KMEANS_PLUS_PLUS
	INIT_RANDOM
)

Variables

var (
	ErrInvalidInitMethod     = errors.New("invalid init method")
	ErrInvalidDataLength     = errors.New("data length must be divisible by the number of features")
	ErrInvalidNumClusters    = errors.New("number of clusters must be greater than 0")
	ErrInvalidNumFeatures    = errors.New("number of features must be greater than 0")
	ErrEmptyData             = errors.New("data is empty")
	ErrFewerClustersThanData = errors.New("number of clusters must be less than the number of data points")
	ErrInvalidMaxIterations  = errors.New("max iterations must be greater than 0")
	ErrInvalidTolerance      = errors.New("tolerance must be greater than 0")
	ErrInvalidConcurrency    = errors.New("concurrency must be greater than 0")
)

Types

type KMeans

type KMeans struct {
	// contains filtered or unexported fields
}

func LoadKMeans

func LoadKMeans(dec *gob.Decoder) (*KMeans, error)

func NewKMeans

func NewKMeans(numClusters, numFeatures int, opts ...NewOption) (*KMeans, error)

func (*KMeans) Centroids

func (km *KMeans) Centroids() [][]float32

func (*KMeans) Decode

func (km *KMeans) Decode(dec *gob.Decoder) error

func (*KMeans) Encode

func (km *KMeans) Encode(enc *gob.Encoder) error

func (*KMeans) Predict

func (km *KMeans) Predict(data []float32, fn func(row, minCol int, minVal float32) error) error

func (*KMeans) Train

func (km *KMeans) Train(data []float32, opts ...TrainOption) (int, float32, error)

type KMeansState

type KMeansState struct {
	InitMethod  int
	NumClusters int
	NumFeatures int
	Centroids   [][]float32
}

type NewOption

type NewOption func(*KMeansState) error

func WithInitMethod

func WithInitMethod(method int) NewOption

type TrainOption

type TrainOption func(*trainConfig) error

func WithConcurrency

func WithConcurrency(concurrency int) TrainOption

func WithMaxIterations

func WithMaxIterations(iter int) TrainOption

func WithTolerance

func WithTolerance(tol float32) TrainOption