AI.Clustering.KMeans.Types

description starting at first column

Documentation

data KMeansOpts

Constructors

KMeansOpts

Fields

• kmeansMethod :: Method
• kmeansSeed :: Vector Word32

  Seed for random number generation

• kmeansClusters :: Bool

  Wether to return clusters, may use a lot memory

defaultKMeansOpts :: KMeansOpts

data KMeans a

Results from running kmeans

Constructors

KMeans

Fields

• membership :: Vector Int

  A vector of integers (0 ~ k-1) indicating the cluster to which each point is allocated.

• centers :: Matrix Double

  A matrix of cluster centers.

• clusters :: Maybe [[a]]

Instances

Show a => Show (KMeans a)

Methods

showsPrec :: Int -> KMeans a -> ShowS

show :: KMeans a -> String

showList :: [KMeans a] -> ShowS

data Method

Different initialization methods

Constructors

Forgy

The Forgy method randomly chooses k unique observations from the data set and uses these as the initial means.

KMeansPP

K-means++ algorithm.

Centers (Matrix Double)

Provide a set of k centroids