spectral-clustering-0.2.1.2: Library for spectral clustering.

Math.Clustering.Spectral.Eigen.FeatureMatrix

Synopsis

# Documentation

newtype B Source #

Constructors

 B FieldsunB :: SparseMatrixXd
Instances
 Source # Instance details MethodsshowsPrec :: Int -> B -> ShowS #show :: B -> String #showList :: [B] -> ShowS #

newtype B1 Source #

Constructors

 B1 FieldsunB1 :: SparseMatrixXd
Instances
 Source # Instance details MethodsshowsPrec :: Int -> B1 -> ShowS #show :: B1 -> String #showList :: [B1] -> ShowS #

newtype B2 Source #

Constructors

 B2 FieldsunB2 :: SparseMatrixXd
Instances
 Source # Instance details MethodsshowsPrec :: Int -> B2 -> ShowS #show :: B2 -> String #showList :: [B2] -> ShowS #

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.

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

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

Get the cosine similarity between two rows using B2.