spectral-clustering-0.3.1.0: Library for spectral clustering.

Safe HaskellNone
LanguageHaskell2010

Math.Clustering.Spectral.Eigen.FeatureMatrix

Synopsis

Documentation

newtype B Source #

Normed rows of B2. For a complete explanation, see Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

Constructors

B 

Fields

Instances
Show B Source # 
Instance details

Defined in Math.Clustering.Spectral.Eigen.FeatureMatrix

Methods

showsPrec :: Int -> B -> ShowS #

show :: B -> String #

showList :: [B] -> ShowS #

newtype B1 Source #

B1 observation by feature matrix.

Constructors

B1 

Fields

Instances
Show B1 Source # 
Instance details

Defined in Math.Clustering.Spectral.Eigen.FeatureMatrix

Methods

showsPrec :: Int -> B1 -> ShowS #

show :: B1 -> String #

showList :: [B1] -> ShowS #

newtype B2 Source #

B2 term frequency-inverse document frequency matrix of B1.

Constructors

B2 

Fields

Instances
Show B2 Source # 
Instance details

Defined in Math.Clustering.Spectral.Eigen.FeatureMatrix

Methods

showsPrec :: Int -> B2 -> ShowS #

show :: B2 -> String #

showList :: [B2] -> ShowS #

type LabelVector = SparseMatrixXd Source #

Output vector containing cluster assignment (0 or 1).

spectral :: Int -> Int -> B -> SparseMatrixXd Source #

Returns the second left singular vector (or Nth) of a sparse spectral process. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

spectralCluster :: B -> LabelVector Source #

Returns a vector of cluster labels for two groups by finding the second left singular vector of a special normalized matrix. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

spectralClusterK :: Int -> Int -> B -> LabelVector Source #

Returns a vector of cluster labels for two groups by finding the largest singular vectors and on of a special normalized matrix and running kmeans. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

getB :: Bool -> SparseMatrixXd -> B Source #

Get the normalized matrix B from an input matrix where the features are columns and rows are observations. Optionally, do not normalize.

b1ToB2 :: B1 -> B2 Source #

Normalize the input matrix by column. Here, columns are features.

getSimilarityFromB2 :: B2 -> Int -> Int -> Double Source #

Get the cosine similarity between two rows using B2.