cluster

README

Clustering Package

WIP

Documentation

Index

• func Covariance(data [][]float64, rowvar bool) ([][]float64, error)
• func Eigen(data [][]float64) ([]float64, [][]float64, error)
• func Euclidean(point []float64, data [][]float64) []float64
• func Float64TwoDimenToDense(data [][]float64) *mat.Dense
• func Float64TwoDimenToSymDense(data [][]float64) *mat.SymDense
• func PCA(data [][]float64, n int) ([][]float64, error)
• func ScatterPlot2DimenData(data [][]float64, label []string, params []string) error
• func Spectral(data [][]float64, radius float64) ([]string, error)
• type KMeans
  • func InitKMeans(n, max_eps int) *KMeans
  • func (model *KMeans) Evaluate(data [][]float64) ([][]float64, []string, error)
  • func (model *KMeans) Train(data [][]float64) error

Functions

func Covariance

func Covariance(data [][]float64, rowvar bool) ([][]float64, error)

*

• Covariance()
• Helper function utilized for PCA
• Returns covariance matrix of data
• Rowvar utilized if variable data is arranged by row

func Eigen

func Eigen(data [][]float64) ([]float64, [][]float64, error)

*

• Eigen()
• Return the eigenvalues and eigenvectors of a real symmetric matrix

func Euclidean

func Euclidean(point []float64, data [][]float64) []float64

*

• Euclidean()
• Grabs all the euclidean distances between a point and set of points

func Float64TwoDimenToDense

func Float64TwoDimenToDense(data [][]float64) *mat.Dense

*

• Float64TwoDimenToDense()
• Helper function utilized for PCA
• Return mat.Dense instance of 2D float64 slice

func Float64TwoDimenToSymDense

func Float64TwoDimenToSymDense(data [][]float64) *mat.SymDense

*

• Float64TwoDimenToDense()
• Helper function utilized for PCA
• Return mat.SymDense instance of 2D float64 slice

func PCA

func PCA(data [][]float64, n int) ([][]float64, error)

*

• PCA()
• Performs principal component analysis on given data
• Reduces dimensionality to n components
• Assumes data is organized via column

func ScatterPlot2DimenData

func ScatterPlot2DimenData(data [][]float64, label []string, params []string) error

*

• ScatterPlot2DimenData()
• Creates a scatterplot with provided data, labels, and plot parameters
• Params consist of output directory, title, and x/y labels

func Spectral

func Spectral(data [][]float64, radius float64) ([]string, error)

*

• Spectral()
• Performs spectral clustering on data and returns the label for each data point
• Clusters data into 2 partitions
• Radius value determines connectivity

Types

type KMeans

type KMeans struct {
	CENTROIDS    [][]float64
	CLUSTERS     int
	MAX_EPISODES int
}

*

• KMeans struct
• Stores centroids and number of clusters in model

func InitKMeans

func InitKMeans(n, max_eps int) *KMeans

*

• InitKMeans()
• Initializes KMeans model with n clusters and max_eps iterations

func (*KMeans) Evaluate

func (model *KMeans) Evaluate(data [][]float64) ([][]float64, []string, error)

*

• Evaluate()
• Evaluates data with trained model to perform cluster analysis
• Returns analysis with labels for plotting
• Throws error if model not trained

func (*KMeans) Train

func (model *KMeans) Train(data [][]float64) error

*

• Train()
• Generates KMeans model with training data using KMeans++ algorithm
• Throws error if data dimensions are empty/not consistent or fail in centroid calculations