Documentation ¶
Index ¶
- func BrayCurtisDistance(firstVector, secondVector []float64) (float64, error)
- func CanberraDistance(firstVector, secondVector []float64) (float64, error)
- func ChebyshevDistance(firstVector, secondVector []float64) (float64, error)
- func EuclideanDistance(firstVector, secondVector []float64) (float64, error)
- func HammingDistance(firstVector, secondVector []float64) (float64, error)
- func Kmeans(rawData [][]float64, k int, distanceFunction DistanceFunction, threshold int) ([]int, error)
- func LPNorm(vector []float64, p float64) (float64, error)
- func ManhattanDistance(firstVector, secondVector []float64) (float64, error)
- func MinkowskiDistance(firstVector, secondVector []float64, p float64) (float64, error)
- func SquaredEuclideanDistance(firstVector, secondVector []float64) (float64, error)
- func WeightedMinkowskiDistance(firstVector, secondVector, weightVector []float64, p float64) (float64, error)
- type ClusteredObservation
- type DistanceFunction
- type Observation
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func BrayCurtisDistance ¶
func CanberraDistance ¶
func ChebyshevDistance ¶
infinity norm distance (l_inf distance)
func EuclideanDistance ¶
2-norm distance (l_2 distance)
func HammingDistance ¶
func Kmeans ¶
func Kmeans(rawData [][]float64, k int, distanceFunction DistanceFunction, threshold int) ([]int, error)
K-Means Algorithm with smart seeds as known as K-Means ++
func ManhattanDistance ¶
1-norm distance (l_1 distance)
func MinkowskiDistance ¶
p-norm distance (l_p distance)
func SquaredEuclideanDistance ¶
Higher weight for the points that are far apart Not a real metric as it does not obey triangle inequality
Types ¶
type ClusteredObservation ¶
type ClusteredObservation struct { ClusterNumber int Observation }
Abstracts the Observation with a cluster number Update and computeation becomes more efficient
type DistanceFunction ¶
Distance Function: To compute the distanfe between observations
type Observation ¶
type Observation []float64
Observation: Data Abstraction for an N-dimensional observation
func (Observation) Add ¶
func (observation Observation) Add(otherObservation Observation)
Summation of two vectors
func (Observation) InnerProduct ¶
func (observation Observation) InnerProduct(otherObservation Observation)
Dot Product of Two vectors
func (Observation) Mul ¶
func (observation Observation) Mul(scalar float64)
Multiplication of a vector with a scalar
func (Observation) OuterProduct ¶
func (observation Observation) OuterProduct(otherObservation Observation) [][]float64
Outer Product of two arrays TODO: Need to be tested