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

• kmeansMaxIter :: Int

  Maximum iteration

defaultKMeansOpts :: KMeansOpts

Default options. > defaultKMeansOpts = KMeansOpts > { kmeansMethod = KMeansPP > , kmeansSeed = U.fromList [1,2,3,4,5,6,7] > , kmeansClusters = True > , kmeansMaxIter = 10 > }

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]]
• sse :: Double

  the sum of squared error (SSE)

Instances

Show a => Show (KMeans a)

Defined in AI.Clustering.KMeans.Types

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