spectral-clustering 0.2.1.3 → 0.2.1.4

raw patch · 5 files changed

+22/−1 lines, 5 filesPVP: minor bump suggested

API additions: PVP suggests at least a minor version bump

API changes (from Hackage documentation)

+ Math.Clustering.Spectral.Dense: type LabelVector = Vector Double

+ Math.Clustering.Spectral.Eigen.AdjacencyMatrix: type LabelVector = SparseMatrixXd

+ Math.Clustering.Spectral.Eigen.FeatureMatrix: type LabelVector = SparseMatrixXd

+ Math.Clustering.Spectral.Sparse: type LabelVector = SpVector Double

Files

spectral-clustering.cabal +1/−1
src/Math/Clustering/Spectral/Dense.hs +1/−0
src/Math/Clustering/Spectral/Eigen/AdjacencyMatrix.hs +1/−0
src/Math/Clustering/Spectral/Eigen/FeatureMatrix.hs +8/−0
src/Math/Clustering/Spectral/Sparse.hs +11/−0

spectral-clustering.cabal view

@@ -1,6 +1,6 @@ cabal-version: >=1.10 name: spectral-clustering-version: 0.2.1.3+version: 0.2.1.4 license: GPL-3 license-file: LICENSE copyright: 2018 Gregory W. Schwartz

src/Math/Clustering/Spectral/Dense.hs view

@@ -10,6 +10,7 @@     , spectralNorm     , getDegreeMatrix     , AdjacencyMatrix (..)+    , LabelVector (..)     ) where  -- Remote

src/Math/Clustering/Spectral/Eigen/AdjacencyMatrix.hs view

@@ -13,6 +13,7 @@     , getDegreeMatrix     , secondLeft     , AdjacencyMatrix (..)+    , LabelVector (..)     ) where  -- Remote

src/Math/Clustering/Spectral/Eigen/FeatureMatrix.hs view

@@ -10,6 +10,7 @@     ( B (..)     , B1 (..)     , B2 (..)+    , LabelVector (..)     , spectral     , spectralCluster     , spectralClusterK@@ -34,11 +35,18 @@  -- Local +-- | Output vector containing cluster assignment (0 or 1). type LabelVector = S.SparseMatrixXd+-- | B1 observation by feature matrix. newtype B1 = B1 { unB1 :: S.SparseMatrixXd } deriving (Show)+-- | B2 term frequency-inverse document frequency matrix of B1. newtype B2 = B2 { unB2 :: S.SparseMatrixXd } deriving (Show)+-- | Diagonal matrix from \(diag(B(B^{T}1))\). newtype D  = D { unD :: S.SparseMatrixXd } deriving (Show)+-- | Matrix from \(D^{-1/2}B}\). newtype C  = C { unC :: S.SparseMatrixXd } deriving (Show)+-- | Normed rows of B2. For a complete explanation, see Shu et al., "Efficient+-- Spectral Neighborhood Blocking for Entity Resolution", 2011. newtype B  = B { unB :: S.SparseMatrixXd } deriving (Show)  -- | Normalize the input matrix by column. Here, columns are features.

src/Math/Clustering/Spectral/Sparse.hs view

@@ -11,6 +11,7 @@     , B1 (..)     , B2 (..)     , AdjacencyMatrix (..)+    , LabelVector (..)     , spectral     , spectralCluster     , spectralClusterK@@ -39,12 +40,22 @@  -- Local +-- | Output vector containing cluster assignment (0 or 1). type LabelVector = S.SpVector Double++-- | Adjacency matrix input. type AdjacencyMatrix = S.SpMatrix Double++-- | B1 observation by feature matrix. newtype B1 = B1 { unB1 :: S.SpMatrix Double } deriving (Show)+-- | B2 term frequency-inverse document frequency matrix of B1. newtype B2 = B2 { unB2 :: S.SpMatrix Double } deriving (Show)+-- | Diagonal matrix from \(diag(B(B^{T}1))\). newtype D  = D { unD :: S.SpMatrix Double } deriving (Show)+-- | Matrix from \(D^{-1/2}B}\). newtype C  = C { unC :: S.SpMatrix Double } deriving (Show)+-- | Normed rows of B2. For a complete explanation, see Shu et al., "Efficient+-- Spectral Neighborhood Blocking for Entity Resolution", 2011. newtype B  = B { unB :: S.SpMatrix Double } deriving (Show)  -- | Normalize the input matrix by column. Here, columns are features.