packages feed

hierarchical-spectral-clustering (empty) → 0.2.1.0

raw patch · 14 files changed

+2048/−0 lines, 14 filesdep +aesondep +aeson-prettydep +basesetup-changed

Dependencies added: aeson, aeson-pretty, base, bytestring, cassava, clustering, containers, eigen, fgl, filepath, hierarchical-clustering, hierarchical-spectral-clustering, hmatrix, lens, managed, modularity, mtl, optparse-generic, safe, sparse-linear-algebra, spectral-clustering, streaming, streaming-bytestring, streaming-cassava, streaming-with, text, text-show, tree-fun, vector

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+ Setup.hs view
@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain
+ app/Main.hs view
@@ -0,0 +1,231 @@+{- cluster-tree+Gregory W. Schwartz++Hierarchical spectral clustering of data.+-}++{-# LANGUAGE BangPatterns      #-}+{-# LANGUAGE DataKinds         #-}+{-# LANGUAGE DeriveGeneric     #-}+{-# LANGUAGE OverloadedStrings #-}+{-# LANGUAGE TypeOperators     #-}+{-# LANGUAGE StandaloneDeriving #-}++module Main where++-- Standard+import Data.Maybe (fromMaybe, catMaybes)+import GHC.Generics++-- Cabal+import Data.Char (ord)+import Data.List (intercalate)+import Data.Monoid ((<>))+import Options.Generic+import Safe (atMay)+import System.IO (stdin)+import Text.Read (readMaybe)+import TextShow (showt)+import qualified Control.Lens as L+import qualified Data.Aeson as A+import qualified Data.Aeson.Encode.Pretty as A+import qualified Data.ByteString.Lazy.Char8 as B+import qualified Data.Csv as CSV+import qualified Data.Map.Strict as Map+import qualified Data.Set as Set+import qualified Data.Text as T+import qualified Data.Vector as V+import qualified Numeric.LinearAlgebra as H+import qualified System.FilePath as File+import Math.Graph.Types++-- Local+import Math.Clustering.Hierarchical.Spectral.Load+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Graph.Components+import qualified Math.Clustering.Hierarchical.Spectral.Dense as HD+import qualified Math.Clustering.Hierarchical.Spectral.Eigen.AdjacencyMatrix as HS++newtype Delimiter  = Delimiter { unDelimiter :: Char } deriving (Read, Show)+newtype Row        = Row { unRow :: Int } deriving (Eq, Ord, Read, Show)+newtype Column     = Column { unColumn :: Int } deriving (Eq, Ord, Read, Show)+newtype OutputTree = OutputTree { unOutputTree :: String } deriving (Read, Show)+newtype MinSize    = MinSize { unMinSize :: Int } deriving (Read, Show)+newtype NumEigen   = NumEigen { unNumEigen :: Int } deriving (Read, Show)++data ClusteringType = Sparse | Dense deriving (Read, Show)+data Components a = Single (ClusteringTree a) | Multiple [ClusteringTree a]++instance A.ToJSON Q where+      toEncoding = A.genericToEncoding A.defaultOptions+instance A.FromJSON Q++instance (A.ToJSON a) => A.ToJSON (ClusteringVertex a) where+      toEncoding = A.genericToEncoding A.defaultOptions+instance (A.FromJSON a) => A.FromJSON (ClusteringVertex a)++-- | Command line arguments+data Options = Options { clusteringType :: Maybe String+                                       <?> "([Sparse] | Dense) Method for clustering data."+                       , delimiter      :: Maybe Char+                                       <?> "([,] | CHAR) The delimiter of the CSV file. Format is row,column,value with no header."+                       , minSize        :: Maybe Int+                                       <?> "([Nothing] | INT) Minimum size of a cluster."+                       , numEigen       :: Maybe Int+                                       <?> "([1] | INT) Number of eigenvectors to use while clustering with kmeans. Takes from the first eigenvector. Recommended to start at 2 and work up from there if needed."+                       , minModularity  :: Maybe Double+                                       <?> "([0] | DOUBLE) Minimum modularity to be over to continue recursion."+                       , eigenGroup     :: Maybe String+                                       <?> "([SignGroup] | KMeansGroup) Whether to group the eigenvector using the sign or kmeans while clustering. While the default is sign, kmeans may be more accurate (but starting points are arbitrary)."+                       , separateComponents :: Bool+                                           <?> "Whether to first separate connected components of the graph first. Will output a dendrogram for each component with the name of the tree and the number of nodes within the tree, along with the base set by --output-tree."+                       , outputTree     :: Maybe String+                                       <?> "([Nothing] | FILE) The name of the file to output the tree in JSON format."+                       }+               deriving (Generic)++modifiers :: Modifiers+modifiers = lispCaseModifiers { shortNameModifier = short }+  where+    short "minSize" = Just 'S'+    short x         = firstLetter x++instance ParseRecord Options where+    parseRecord = parseRecordWithModifiers modifiers++main :: IO ()+main = do+    opts <- getRecord "cluster-tree, Gregory W. Schwartz.\+                      \ Hierarchical spectral clustering of data Computes real\+                      \ symmetric part of matrix, so ensure the input is real\+                      \ and symmetric. Diagonal should be 0s for\+                      \ adjacency matrix.\+                      \ Format is row,column,value with no header.\+                      \ Must end with a newline."++    let readOrErr err       = fromMaybe (error err) . readMaybe+        clusteringType'     =+          maybe Sparse (readOrErr "Cannot read --clustering-type")+            . unHelpful+            . clusteringType+            $ opts+        delim'              =+            Delimiter . fromMaybe ',' . unHelpful . delimiter $ opts+        minSize'            = fmap MinSize . unHelpful . minSize $ opts+        numEigen'           = fmap NumEigen . unHelpful . numEigen $ opts+        minModularity'      = fmap Q . unHelpful . minModularity $ opts+        eigenGroup'         =+          maybe SignGroup (readOrErr "Cannot read --eigen-group")+            . unHelpful+            . eigenGroup+            $ opts+        separateComponents' = unHelpful . separateComponents $ opts+        outputTree'         = fmap OutputTree . unHelpful . outputTree $ opts+        decodeOpt       = CSV.defaultDecodeOptions+                            { CSV.decDelimiter =+                                fromIntegral (ord . unDelimiter $ delim')+                            }+        encodeOpt       = CSV.defaultEncodeOptions+                            { CSV.encDelimiter =+                                fromIntegral (ord . unDelimiter $ delim')+                            }++    clusteringTree <-+        case clusteringType' of+            Dense -> do+                (items, mat) <- readDenseAdjMatrix decodeOpt stdin++                let cluster items = HD.hierarchicalSpectralCluster+                                      eigenGroup'+                                      (fmap unNumEigen numEigen')+                                      (fmap unMinSize minSize')+                                      minModularity'+                                      items++                return $+                    if separateComponents'+                        then Multiple+                           . fmap (uncurry cluster)+                           . getComponentMatsItems items+                           $ mat+                        else Single $ cluster items mat+            Sparse -> do+                (items, mat) <- readEigenSparseAdjMatrix decodeOpt stdin++                let cluster items = HS.hierarchicalSpectralCluster+                                      eigenGroup'+                                      (fmap unNumEigen numEigen')+                                      (fmap unMinSize minSize')+                                      minModularity'+                                      items++                return $+                    if separateComponents'+                        then Multiple+                           . fmap (uncurry cluster)+                           . getComponentMatsItems items+                           $ mat+                        else Single $ cluster items mat++    body <- case clusteringTree of+        (Single ct) -> do+            let clustering = zip ([1..] :: [Int]) . getClusterItemsTree $ ct+                body :: [(T.Text, T.Text)]+                body = concatMap+                        (\(!c, xs) -> fmap (\ !x -> (x, showt c)) . V.toList $ xs)+                        clustering++            case outputTree' of+                Nothing                -> return ()+                Just (OutputTree file) -> B.writeFile file+                                        . A.encodePretty+                                        . clusteringTreeToDendrogram+                                        $ ct++            return body+        (Multiple cts) -> do+            let clustering =+                    fmap (L.over L._2 (zip ([1..] :: [Int]) . getClusterItemsTree))+                        . zip ([1..] :: [Int])+                        $ cts+                body :: [(T.Text, T.Text)]+                body = concatMap+                        (\ (!t, xs)+                        -> concatMap (\ (!c, ys)+                                     -> fmap (\ x -> (x, showt t <> "/" <> showt c))+                                      . V.toList+                                      $ ys+                                     )+                           xs+                        )+                        clustering++            case outputTree' of+                Nothing                -> return ()+                Just (OutputTree file) -> do+                    let getSize :: ClusteringTree a -> Int+                        getSize = sum . fmap V.length . getClusterItemsTree+                        getFileName :: Int -> Int -> String+                        getFileName t n =+                            uncurry (File.</>)+                                . L.over L._2 (\x -> intercalate "_" ["tree", show t, "size", show n, x])+                                . File.splitFileName+                                $ file+                        write :: (A.ToJSON a) => Int -> ClusteringTree a -> IO ()+                        write t tree = do+                            B.writeFile (getFileName t (getSize tree))+                                . A.encodePretty+                                . clusteringTreeToDendrogram+                                $ tree++                    mapM_ (uncurry write) . zip [1..] $ cts++            return body++    -- | Print final result.+    B.putStr+        . (<>) "item,cluster\n"+        . CSV.encodeWith encodeOpt+        $ body++    return ()
+ hierarchical-spectral-clustering.cabal view
@@ -0,0 +1,76 @@+name:                hierarchical-spectral-clustering+version:             0.2.1.0+synopsis:            Hierarchical spectral clustering of a graph.+description:         Generate a tree of hierarchical spectral clustering using Newman-Girvan modularity as a stopping criteria.+homepage:            http://github.com/GregorySchwartz/hierarchical-spectral-clustering#readme+license:             GPL-3+license-file:        LICENSE+author:              Gregory W. Schwartz+maintainer:          gsch@pennmedicine.upenn.edu+copyright:           2018 Gregory W. Schwartz+category:            Bioinformatics+build-type:          Simple+-- extra-source-files:+cabal-version:       >=1.10++library+  hs-source-dirs:      src+  exposed-modules:     Math.Clustering.Hierarchical.Spectral.Dense+                     , Math.Clustering.Hierarchical.Spectral.Sparse+                     , Math.Clustering.Hierarchical.Spectral.Load+                     , Math.Clustering.Hierarchical.Spectral.Eigen.AdjacencyMatrix+                     , Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix+                     , Math.Clustering.Hierarchical.Spectral.Types+                     , Math.Clustering.Hierarchical.Spectral.Utility+                     , Math.Graph.Components+                     , Math.Graph.Types+  other-modules:       Math.Clustering.Hierarchical.Spectral.Test+  build-depends:       base >= 4.7 && < 5+                     , aeson+                     , cassava+                     , clustering+                     , containers+                     , eigen == 3.3.4.1+                     , hierarchical-clustering+                     , hmatrix+                     , fgl+                     , managed+                     , modularity+                     , mtl+                     , safe+                     , sparse-linear-algebra+                     , spectral-clustering+                     , streaming+                     , streaming-bytestring+                     , streaming-cassava+                     , streaming-with+                     , text+                     , tree-fun+                     , vector+  ghc-options:         -O2+  default-language:    Haskell2010++executable cluster-tree+  hs-source-dirs:      app+  main-is:             Main.hs+  ghc-options:         -threaded -rtsopts -O2+  build-depends:       base+                     , hierarchical-spectral-clustering+                     , aeson+                     , aeson-pretty+                     , bytestring+                     , cassava+                     , containers+                     , filepath+                     , hmatrix+                     , lens+                     , optparse-generic+                     , safe+                     , text+                     , text-show+                     , vector+  default-language:    Haskell2010++source-repository head+  type:     git+  location: https://github.com/GregorySchwartz/hierarchical-spectral-clustering
+ src/Math/Clustering/Hierarchical/Spectral/Dense.hs view
@@ -0,0 +1,94 @@+{- Math.Clustering.Hierarchical.Spectral+Gregory W. Schwartz++Collects the functions pertaining to hierarchical spectral clustering.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Dense+    ( hierarchicalSpectralCluster+    , AdjacencyMatrix (..)+    , Items (..)+    ) where++-- Remote+import Data.Bool (bool)+import Data.Clustering.Hierarchical (Dendrogram (..))+import Data.Maybe (fromMaybe)+import Data.Tree (Tree (..))+import Math.Clustering.Spectral.Dense (spectralClusterNorm, spectralClusterKNorm)+import Math.Modularity.Dense (getModularity)+import Math.Modularity.Types (Q (..))+import qualified Data.Foldable as F+import qualified Data.Set as Set+import qualified Data.Vector as V+import qualified Data.Vector.Storable as VS+import qualified Numeric.LinearAlgebra as H++-- Local+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Clustering.Hierarchical.Spectral.Utility++type AdjacencyMatrix = H.Matrix Double+type Items a         = V.Vector a++-- | Check if there is more than one cluster.+hasMultipleClusters :: H.Vector Double -> Bool+hasMultipleClusters = (> 1) . Set.size . Set.fromList . H.toList++-- | Generates a tree through divisive hierarchical clustering using+-- Newman-Girvan modularity as a stopping criteria. Can also use minimum number+-- of observations in a cluster as the stopping criteria.+hierarchicalSpectralCluster :: (Show a) => EigenGroup+                            -> Maybe NumEigen+                            -> Maybe Int+                            -> Maybe Q+                            -> Items a+                            -> AdjacencyMatrix+                            -> ClusteringTree a+hierarchicalSpectralCluster !eigenGroup !numEigenMay !minSizeMay !minModMay !items !adjMat =+    if H.rows adjMat > 1+        && hasMultipleClusters clusters+        && ngMod > minMod+        && H.rows left >= minSize+        && H.rows right >= minSize+        then+            Node { rootLabel = vertex+                 , subForest =+                    [ hierarchicalSpectralCluster+                        eigenGroup+                        numEigenMay+                        minSizeMay+                        minModMay+                        (subsetVector items leftIdxs)+                        left+                    , hierarchicalSpectralCluster+                        eigenGroup+                        numEigenMay+                        minSizeMay+                        minModMay+                        (subsetVector items rightIdxs)+                        right+                    ]+                 }+        else+            Node {rootLabel = vertex, subForest = []}+  where+    minMod      = fromMaybe (Q 0) minModMay+    minSize     = fromMaybe 1 minSizeMay+    numEigen    = fromMaybe 1 numEigenMay+    vertex      = ClusteringVertex { _clusteringItems = items+                                   , _ngMod = ngMod+                                   }+    clusters    = spectralClustering eigenGroup adjMat+    spectralClustering :: EigenGroup -> AdjacencyMatrix -> H.Vector Double+    spectralClustering SignGroup   = spectralClusterNorm+    spectralClustering KMeansGroup = spectralClusterKNorm numEigen 2+    ngMod       = getModularity clusters $ adjMat+    getIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []+    leftIdxs    = getIdxs 0 $ clusters+    rightIdxs   = getIdxs 1 $ clusters+    left        = adjMat H.?? (H.Pos (H.idxs leftIdxs), H.Pos (H.idxs leftIdxs))+    right       =+        adjMat H.?? (H.Pos (H.idxs rightIdxs), H.Pos (H.idxs rightIdxs))
+ src/Math/Clustering/Hierarchical/Spectral/Eigen/AdjacencyMatrix.hs view
@@ -0,0 +1,100 @@+{- Math.Clustering.Hierarchical.Spectral.Eigen.AdjacencyMatrix+Gregory W. Schwartz++Collects the functions pertaining to hierarchical spectral clustering.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Eigen.AdjacencyMatrix+    ( hierarchicalSpectralCluster+    , AdjacencyMatrix (..)+    , Items (..)+    ) where++-- Remote+import Data.Bool (bool)+import Data.Clustering.Hierarchical (Dendrogram (..))+import Data.Maybe (fromMaybe)+import Data.Tree (Tree (..))+import Math.Clustering.Spectral.Eigen.AdjacencyMatrix (spectralClusterNorm, spectralClusterKNorm)+import Math.Modularity.Eigen.Sparse (getModularity)+import Math.Modularity.Types (Q (..))+import Safe (headMay)+import qualified Data.Foldable as F+import qualified Data.Set as Set+import qualified Data.Eigen.SparseMatrix as S+import qualified Data.Vector as V+import qualified Data.Vector.Storable as VS++-- Local+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Clustering.Hierarchical.Spectral.Utility++type AdjacencyMatrix = S.SparseMatrixXd+type Items a         = V.Vector a++-- | Check if there is more than one cluster.+hasMultipleClusters :: S.SparseMatrixXd -> Bool+hasMultipleClusters = (> 1)+                    . Set.size+                    . Set.fromList+                    . concat+                    . S.toDenseList++-- | Generates a tree through divisive hierarchical clustering using+-- Newman-Girvan modularity as a stopping criteria. Can also use minimum number+-- of observations in a cluster as the stopping criteria.+hierarchicalSpectralCluster :: EigenGroup+                            -> Maybe NumEigen+                            -> Maybe Int+                            -> Maybe Q+                            -> Items a+                            -> AdjacencyMatrix+                            -> ClusteringTree a+hierarchicalSpectralCluster !eigenGroup !numEigenMay !minSizeMay !minModMay !items !adjMat =++    if S.rows adjMat > 1+        && hasMultipleClusters clusters+        && ngMod > minMod+        && S.rows left >= minSize+        && S.rows right >= minSize+        then do+            Node { rootLabel = vertex+                 , subForest = [ hierarchicalSpectralCluster+                                  eigenGroup+                                  numEigenMay+                                  minSizeMay+                                  minModMay+                                  (subsetVector items leftIdxs)+                                  left+                               , hierarchicalSpectralCluster+                                  eigenGroup+                                  numEigenMay+                                  minSizeMay+                                  minModMay (subsetVector items rightIdxs)+                                  right+                               ]+                 }+        else+            Node {rootLabel = vertex, subForest = []}+  where+    clusters = spectralClustering eigenGroup adjMat+    spectralClustering :: EigenGroup -> AdjacencyMatrix -> S.SparseMatrixXd+    spectralClustering SignGroup   = spectralClusterNorm+    spectralClustering KMeansGroup = spectralClusterKNorm numEigen 2+    minMod      = fromMaybe (Q 0) minModMay+    minSize     = fromMaybe 1 minSizeMay+    numEigen    = fromMaybe 1 numEigenMay+    vertex      = ClusteringVertex { _clusteringItems = items+                                   , _ngMod = ngMod+                                   }+    ngMod       = getModularity clusters adjMat+    getIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []+                . VS.fromList+                . concat+                . S.toDenseList+    leftIdxs    = getIdxs 0 clusters+    rightIdxs   = getIdxs 1 clusters+    left        = S.squareSubset leftIdxs adjMat+    right       = S.squareSubset rightIdxs adjMat
+ src/Math/Clustering/Hierarchical/Spectral/Eigen/FeatureMatrix.hs view
@@ -0,0 +1,113 @@+{- Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix+Gregory W. Schwartz++Collects the functions pertaining to hierarchical spectral clustering for+feature matrices.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix+    ( hierarchicalSpectralCluster+    , FeatureMatrix (..)+    , B (..)+    , Items (..)+    , ShowB (..)+    ) where++-- Remote+import Data.Bool (bool)+import Data.Clustering.Hierarchical (Dendrogram (..))+import Data.Maybe (fromMaybe)+import Data.Tree (Tree (..))+import Math.Clustering.Spectral.Eigen.FeatureMatrix (B (..), getB, spectralCluster, spectralClusterK)+import Math.Modularity.Eigen.Sparse (getBModularity)+import Math.Modularity.Types (Q (..))+import qualified Data.Foldable as F+import qualified Data.Set as Set+import qualified Data.Eigen.SparseMatrix as S+import qualified Data.Vector as V+import qualified Data.Vector.Storable as VS++-- Local+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Clustering.Hierarchical.Spectral.Utility++type FeatureMatrix   = S.SparseMatrixXd+type Items a         = V.Vector a+type ShowB           = ((Int, Int), [(Int, Int, Double)])+type NormalizeFlag   = Bool++-- | Check if there is more than one cluster.+hasMultipleClusters :: S.SparseMatrixXd -> Bool+hasMultipleClusters = (> 1)+                    . Set.size+                    . Set.fromList+                    . concat+                    . S.toDenseList++-- | Generates a tree through divisive hierarchical clustering using+-- Newman-Girvan modularity as a stopping criteria. Can use minimum number of+-- observations in a cluster as a stopping criteria. Assumes the feature matrix+-- has column features and row observations. Items correspond to rows. Can+-- use FeatureMatrix or a pre-generated B matrix. See Shu et al., "Efficient+-- Spectral Neighborhood Blocking for Entity Resolution", 2011.+hierarchicalSpectralCluster :: EigenGroup+                            -> NormalizeFlag+                            -> Maybe NumEigen+                            -> Maybe Int+                            -> Maybe Q+                            -> Items a+                            -> Either FeatureMatrix B+                            -> ClusteringTree a+hierarchicalSpectralCluster eigenGroup normFlag numEigenMay minSizeMay minModMay initItems initMat =+    go initItems initB+  where+    initB = either (getB normFlag) id $ initMat+    minMod      = fromMaybe (Q 0) minModMay+    minSize     = fromMaybe 1 minSizeMay+    numEigen    = fromMaybe 1 numEigenMay+    go :: Items a -> B -> ClusteringTree a+    go !items !b =+        if (S.rows $ unB b) > 1+            && hasMultipleClusters clusters+            && ngMod > minMod+            && S.rows (unB left) >= minSize+            && S.rows (unB right) >= minSize+            then+                Node { rootLabel = vertex+                     , subForest = [ go (subsetVector items leftIdxs) left+                                   , go (subsetVector items rightIdxs) right+                                   ]+                     }++            else+                Node {rootLabel = vertex, subForest = []}+      where+        vertex      = ClusteringVertex+                        { _clusteringItems = items+                        , _ngMod = ngMod+                        }+        clusters :: S.SparseMatrixXd+        clusters = spectralClustering eigenGroup b+        spectralClustering :: EigenGroup -> B -> S.SparseMatrixXd+        spectralClustering SignGroup   = spectralCluster+        spectralClustering KMeansGroup = spectralClusterK numEigen 2+        ngMod :: Q+        ngMod = getBModularity clusters b+        getSortedIdxs :: Double -> S.SparseMatrixXd -> [Int]+        getSortedIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []+                          . VS.fromList+                          . concat+                          . S.toDenseList+        leftIdxs :: [Int]+        leftIdxs    = getSortedIdxs 0 clusters+        rightIdxs :: [Int]+        rightIdxs   = getSortedIdxs 1 clusters+        left :: B+        left        = B $ extractRows (unB b) leftIdxs+        right :: B+        right       = B $ extractRows (unB b) rightIdxs+        extractRows :: S.SparseMatrixXd -> [Int] -> S.SparseMatrixXd+        extractRows mat [] = S.fromList 0 0 []+        extractRows mat xs = S.fromRows . fmap (flip S.getRow mat) $ xs
+ src/Math/Clustering/Hierarchical/Spectral/Load.hs view
@@ -0,0 +1,167 @@+{- Math.Clustering.Hierarchical.Spectral.Load+Gregory W. Schwartz++Collects the functions pertaining to loading a matrix.+-}++{-# LANGUAGE BangPatterns #-}+{-# LANGUAGE OverloadedStrings #-}++module Math.Clustering.Hierarchical.Spectral.Load+    ( readDenseAdjMatrix+    , readSparseAdjMatrix+    , readEigenSparseAdjMatrix+    ) where++-- Remote+import Control.Monad.Except (runExceptT, ExceptT (..))+import Control.Monad.Managed (with, liftIO, Managed (..))+import Data.Maybe (fromMaybe, catMaybes)+import System.IO (Handle (..))+import qualified Data.ByteString.Streaming.Char8 as BS+import qualified Data.Csv as CSV+import qualified Data.Eigen.SparseMatrix as E+import qualified Data.Map.Strict as Map+import qualified Data.Set as Set+import qualified Data.Sparse.Common as SH+import qualified Data.Text as T+import qualified Data.Vector as V+import qualified Numeric.LinearAlgebra as H+import qualified Streaming as S+import qualified Streaming.Cassava as S+import qualified Streaming.Prelude as S+import qualified Streaming.With.Lifted as SW++-- Local+import Math.Clustering.Hierarchical.Spectral.Types++-- | Generic error message.+errorMsg = error "Not correct format (requires row,column,value)"++-- | Parse a row of a label index file.+parseRow :: (T.Text, T.Text, Double) -> ((T.Text, T.Text), Double)+parseRow (i, j, v) = ((i, j), v)++-- | Ignore the disconnected vertices, not used (rather use very small weight).+ignoreDisconnected :: V.Vector T.Text+                   -> H.Matrix Double+                   -> (V.Vector T.Text, H.Matrix Double)+ignoreDisconnected items mat = (newItems, newMat)+  where+    newItems = V.fromList $ fmap ((V.!) items) valid+    newMat = mat H.?? (H.Pos $ H.idxs valid, H.Pos $ H.idxs valid)+    valid = catMaybes+          . zipWith (\x xs -> if sum xs > 0 then Just x else Nothing) [0..]+          . H.toLists+          $ mat++-- | Ensure symmetry.+symmetric :: [((Int, Int), Double)] -> [((Int, Int), Double)]+symmetric = concatMap (\((!i, !j), v) -> [((i, j), v), ((j, i), v)])++-- | Ensure zeros on diagonal.+zeroDiag :: [((Int, Int), Double)] -> [((Int, Int), Double)]+zeroDiag = filter (\((!i, !j), _) -> i /= j)++-- | Get the translated matrix indices.+getNewIndices+    -- :: (Eq a, Ord a)+    -- => [((a, a), Double)] -> [((Int, Int), Double)]+    :: [((T.Text, T.Text), Double)] -> [((Int, Int), Double)]+getNewIndices xs =+    fmap+        (\((!i,!j),!v) ->+              ( ( Map.findWithDefault eMsg i idxMap+                , Map.findWithDefault eMsg j idxMap+                )+              , v+              )+        )+        xs+  where+    eMsg     = error "Index not found during index conversion."+    indices  = getAllIndices xs+    idxMap   = Map.fromList $ zip indices [0 ..]++-- | Get the list of all indices.+getAllIndices :: (Eq a, Ord a) => [((a, a), Double)] -> [a]+getAllIndices xs = Set.toAscList . Set.union (getSet fst) $ getSet snd+  where+    getSet f = Set.fromList . fmap (f . fst) $ xs++-- | Get a dense adjacency matrix from a handle.+readDenseAdjMatrix :: CSV.DecodeOptions+                   -> Handle+                   -> IO (V.Vector T.Text, H.Matrix Double)+readDenseAdjMatrix decodeOpt handle = flip with return $ do+    let getAssocList = S.toList_ . S.map parseRow++    assocList <-+        fmap (either (error . show) id)+            . runExceptT+            . getAssocList+            . S.decodeWith decodeOpt S.NoHeader+            $ (BS.hGetContents handle :: BS.ByteString (ExceptT S.CsvParseException Managed) ())++    let items = V.fromList $ getAllIndices assocList+        mat   = H.assoc (V.length items, V.length items) 0+              . Set.toList+              . Set.fromList -- Ensure no duplicates.+              . symmetric -- Ensure symmetry.+              . zeroDiag -- Ensure zeros on diagonal.+              . getNewIndices -- Only look at present rows by converting indices.+              $ assocList++    return (items, mat)++-- | Get a sparse adjacency matrix from a handle.+readSparseAdjMatrix :: CSV.DecodeOptions+                    -> Handle+                    -> IO (V.Vector T.Text, SH.SpMatrix Double)+readSparseAdjMatrix decodeOpt handle = flip with return $ do+    let getAssocList = S.toList_ . S.map parseRow++    assocList <-+        fmap (either (error . show) id)+            . runExceptT+            . getAssocList+            . S.decodeWith decodeOpt S.NoHeader+            $ (BS.hGetContents handle :: BS.ByteString (ExceptT S.CsvParseException Managed) ())++    let items = V.fromList $ getAllIndices assocList+        mat   = SH.fromListSM (V.length items, V.length items)+              . Set.toList+              . Set.fromList -- Ensure no duplicates.+              . fmap (\((i, j), v) -> (i, j, v))+              . symmetric -- Ensure symmetry.+              . zeroDiag -- Ensure zeros on diagonal.+              . getNewIndices -- Only look at present rows by converting indices.+              $ assocList++    return (items, mat)++-- | Get a sparse adjacency matrix from a handle.+readEigenSparseAdjMatrix :: CSV.DecodeOptions+                    -> Handle+                    -> IO (V.Vector T.Text, E.SparseMatrixXd)+readEigenSparseAdjMatrix decodeOpt handle = flip with return $ do+    let getAssocList = S.toList_ . S.map parseRow++    assocList <-+        fmap (either (error . show) id)+            . runExceptT+            . getAssocList+            . S.decodeWith decodeOpt S.NoHeader+            $ (BS.hGetContents handle :: BS.ByteString (ExceptT S.CsvParseException Managed) ())++    let items = V.fromList $ getAllIndices assocList+        mat   = E.fromList (V.length items) (V.length items)+              . Set.toList+              . Set.fromList -- Ensure no duplicates.+              . fmap (\((i, j), v) -> (i, j, v))+              . symmetric -- Ensure symmetry.+              . zeroDiag -- Ensure zeros on diagonal.+              . getNewIndices -- Only look at present rows by converting indices.+              $ assocList++    return (items, mat)
+ src/Math/Clustering/Hierarchical/Spectral/Sparse.hs view
@@ -0,0 +1,177 @@+{- Math.Clustering.Hierarchical.Spectral.Sparse+Gregory W. Schwartz++Collects the functions pertaining to hierarchical spectral clustering for sparse+data.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Sparse+    ( hierarchicalSpectralCluster+    , hierarchicalSpectralClusterAdj+    , FeatureMatrix (..)+    , B (..)+    , Items (..)+    , ShowB (..)+    ) where++-- Remote+import Data.Bool (bool)+import Data.Clustering.Hierarchical (Dendrogram (..))+import Data.Maybe (fromMaybe)+import Data.Tree (Tree (..))+import Math.Clustering.Spectral.Sparse (B (..), AdjacencyMatrix (..), getB, spectralCluster, spectralClusterK, spectralClusterNorm, spectralClusterKNorm)+import Math.Modularity.Sparse (getBModularity, getModularity)+import Math.Modularity.Types (Q (..))+import qualified Data.Foldable as F+import qualified Data.Set as Set+import qualified Data.Sparse.Common as S+import qualified Data.Vector as V+import qualified Data.Vector.Storable as VS+import qualified Numeric.LinearAlgebra.Sparse as S++-- Local+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Clustering.Hierarchical.Spectral.Utility++type FeatureMatrix   = S.SpMatrix Double+type Items a         = V.Vector a+type ShowB           = ((Int, Int), [(Int, Int, Double)])+type NormalizeFlag   = Bool++-- | Check if there is more than one cluster.+hasMultipleClusters :: S.SpVector Double -> Bool+hasMultipleClusters = (> 1) . Set.size . Set.fromList . S.toDenseListSV++-- | Generates a tree through divisive hierarchical clustering using+-- Newman-Girvan modularity as a stopping criteria. Can use minimum number of+-- observations in a cluster as a stopping criteria. Assumes the feature matrix+-- has column features and row observations. Items correspond to rows. Can+-- use FeatureMatrix or a pre-generated B matrix. See Shu et al., "Efficient+-- Spectral Neighborhood Blocking for Entity Resolution", 2011.+hierarchicalSpectralCluster :: EigenGroup+                            -> NormalizeFlag+                            -> Maybe NumEigen+                            -> Maybe Int+                            -> Maybe Q+                            -> Items a+                            -> Either FeatureMatrix B+                            -> ClusteringTree a+hierarchicalSpectralCluster eigenGroup normFlag numEigenMay minSizeMay minModMay initItems initMat =+    go initItems initB+  where+    initB = either (getB normFlag) id $ initMat+    minMod   = fromMaybe (Q 0) minModMay+    minSize  = fromMaybe 1 minSizeMay+    numEigen = fromMaybe 1 numEigenMay+    go :: Items a -> B -> ClusteringTree a+    go !items !b =+        if (S.nrows $ unB b) > 1+            && hasMultipleClusters clusters+            && ngMod > minMod+            && S.nrows (unB left) >= minSize+            && S.nrows (unB right) >= minSize+            then+                Node { rootLabel = vertex+                     , subForest = [ go (subsetVector items leftIdxs) left+                                   , go (subsetVector items rightIdxs) right+                                   ]+                     }++            else+                Node {rootLabel = vertex, subForest = []}+      where+        vertex      = ClusteringVertex+                        { _clusteringItems = items+                        , _ngMod = ngMod+                        }+        clusters :: S.SpVector Double+        clusters = spectralClustering eigenGroup b+        spectralClustering :: EigenGroup -> B -> S.SpVector Double+        spectralClustering SignGroup   = spectralCluster+        spectralClustering KMeansGroup = spectralClusterK numEigen 2+        ngMod :: Q+        ngMod       = getBModularity clusters $ b+        getSortedIdxs :: Double -> S.SpVector Double -> [Int]+        getSortedIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []+                          . VS.fromList+                          . S.toDenseListSV+        leftIdxs :: [Int]+        leftIdxs    = getSortedIdxs 0 $ clusters+        rightIdxs :: [Int]+        rightIdxs   = getSortedIdxs 1 $ clusters+        left :: B+        left        = B $ extractRows (unB b) leftIdxs+        right :: B+        right       = B $ extractRows (unB b) rightIdxs+        extractRows :: S.SpMatrix Double -> [Int] -> S.SpMatrix Double+        extractRows mat [] = S.zeroSM 0 0+        extractRows mat xs =+          S.transposeSM . S.fromColsL . fmap (S.extractRow mat) $ xs++-- | Generates a tree through divisive hierarchical clustering using+-- Newman-Girvan modularity as a stopping criteria. Can use minimum number of+-- observations in a cluster as a stopping criteria. Uses an adjacency matrix.+-- Items correspond to rows.+hierarchicalSpectralClusterAdj :: EigenGroup+                               -> Maybe NumEigen+                               -> Maybe Int+                               -> Maybe Q+                               -> Items a+                               -> AdjacencyMatrix+                               -> ClusteringTree a+hierarchicalSpectralClusterAdj eigenGroup numEigenMay minSizeMay minModMay initItems initMat =+    go initItems initMat+  where+    minMod      = fromMaybe (Q 0) minModMay+    minSize     = fromMaybe 1 minSizeMay+    numEigen    = fromMaybe 1 numEigenMay+    go :: Items a -> AdjacencyMatrix -> ClusteringTree a+    go !items !mat =+        if S.nrows mat > 1+            && hasMultipleClusters clusters+            && ngMod > minMod+            && S.nrows left >= minSize+            && S.nrows right >= minSize+            then+                Node { rootLabel = vertex+                     , subForest = [ go (subsetVector items leftIdxs) left+                                   , go (subsetVector items rightIdxs) right+                                   ]+                     }++            else+                Node {rootLabel = vertex, subForest = []}+      where+        vertex      = ClusteringVertex+                        { _clusteringItems = items+                        , _ngMod = ngMod+                        }+        clusters :: S.SpVector Double+        clusters = spectralClustering eigenGroup mat+        spectralClustering :: EigenGroup -> AdjacencyMatrix -> S.SpVector Double+        spectralClustering SignGroup   = spectralClusterNorm+        spectralClustering KMeansGroup = spectralClusterKNorm numEigen 2+        ngMod :: Q+        ngMod = getModularity clusters mat+        getSortedIdxs :: Double -> S.SpVector Double -> [Int]+        getSortedIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []+                          . VS.fromList+                          . S.toDenseListSV+        leftIdxs :: [Int]+        leftIdxs    = getSortedIdxs 0 clusters+        rightIdxs :: [Int]+        rightIdxs   = getSortedIdxs 1 clusters+        left :: AdjacencyMatrix+        left        = getSubMat mat leftIdxs+        right :: AdjacencyMatrix+        right       = getSubMat mat rightIdxs+        getSubMat :: S.SpMatrix Double -> [Int] -> S.SpMatrix Double+        getSubMat mat [] = S.zeroSM 0 0+        getSubMat mat is = S.fromColsL+                         . (\x -> fmap (S.extractCol x) is)+                         . S.transposeSM+                         . S.fromColsL+                         . fmap (S.extractRow mat)+                         $ is
+ src/Math/Clustering/Hierarchical/Spectral/Test.hs view
@@ -0,0 +1,188 @@+{- Math.Clustering.Hierarchical.Spectral.Test+Gregory W. Schwartz++Collects the functions pertaining to testing hierarchical spectral clustering.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Test where++-- Remote+import Data.List (tails)+import Data.Maybe (fromMaybe)+import Data.Monoid ((<>))+import Math.Clustering.Spectral.Sparse (getB, B (..))+import qualified Data.Map.Strict as Map+import qualified Data.Set as Set+import qualified Data.Sparse.Common as S+import qualified Data.Eigen.SparseMatrix as E+import qualified Data.Vector as V+import qualified Math.Clustering.Spectral.Eigen.FeatureMatrix as EF+import qualified Numeric.LinearAlgebra as H++-- Local+import Math.Clustering.Hierarchical.Spectral.Types+import Math.Clustering.Hierarchical.Spectral.Sparse+import qualified Math.Clustering.Hierarchical.Spectral.Dense as Dense+import qualified Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix as EF+import qualified Math.Clustering.Hierarchical.Spectral.Eigen.AdjacencyMatrix as EA++newtype QGram = QGram { unQGram :: String } deriving (Eq, Ord, Read, Show)+newtype QGramMap = QGramMap+    { unQGramMap :: Map.Map QGram Int+    } deriving (Eq,Ord,Read,Show)++exampleData :: [String]+exampleData = [ "600 MOUNTAIN AVENUE"+              , "700 MOUNTAIN AVE"+              , "600-700 MOUNTAIN AVE"+              , "100 DIAMOND HILL RD"+              , "100 DIAMOND HILL ROAD"+              , "123 SPRINGFIELD AVENUE"+              , "123 SPRINFGIELD AVE"+              ]++exampleItems :: V.Vector String+exampleItems = V.fromList exampleData++-- | Add beginning and ending symbols.+addBorders :: String -> String+addBorders x = "##" <> x <> "$$"++-- | Generate qgrams for a string.+getQGrams :: Int -> String -> [QGram]+getQGrams n =+    fmap QGram . filter ((== n) . length) . fmap (take n) . tails . addBorders++-- | Get mapping of qgrams to indices.+getQGramMap :: [QGram] -> QGramMap+getQGramMap = QGramMap . Map.fromList . flip zip [0,1..]++-- | Convert a record to a vector.+recordToRow :: Int -> QGramMap -> String -> S.SpVector Double+recordToRow n (QGramMap qgramMap) = S.fromListSV (Map.size qgramMap)+                                  . Map.toAscList+                                  . Map.fromListWith (+)+                                  . flip zip [1,1..]+                                  . fromMaybe (error "Invalid qgram.")+                                  . mapM (flip Map.lookup qgramMap)+                                  . getQGrams n++-- | Generate the matrix of qgrams from a list of records and qgram length.+exampleMatrix :: Int -> [String] -> S.SpMatrix Double+exampleMatrix n records =+    S.transposeSM . S.fromColsL . fmap (recordToRow n qgramMap) $ records+  where+    qgramMap = getQGramMap+             . Set.toList+             . Set.fromList+             . concatMap (getQGrams n)+             $ records++clusterExample = hierarchicalSpectralCluster+                    SignGroup+                    True+                    Nothing+                    Nothing+                    Nothing+                    exampleItems+                    (Left $ exampleMatrix 3 exampleData)++clusterKExample = hierarchicalSpectralCluster+                    KMeansGroup+                    True+                    (Just 2)+                    Nothing+                    Nothing+                    exampleItems+                    (Left $ exampleMatrix 3 exampleData)++clusterAdjExample = hierarchicalSpectralClusterAdj+                        SignGroup+                        Nothing+                        Nothing+                        Nothing+                        exampleItems+                        adjacencyExample++clusterKAdjExample = hierarchicalSpectralClusterAdj+                        KMeansGroup+                        (Just 2)+                        Nothing+                        Nothing+                        exampleItems+                        adjacencyExample++adjacencyExample :: S.SpMatrix Double+adjacencyExample = S.filterSM (\i j _ -> i /= j) $ (unB b) S.##^ (unB b)+  where+    b = getB True $ exampleMatrix 3 exampleData++denseAdjacencyExample :: H.Matrix Double+denseAdjacencyExample = H.assoc (S.dimSM adjacencyExample) 0+                      . fmap (\(!x, !y, !z) -> if x == y then ((x, y), 0) else ((x, y), z))+                      . S.toListSM+                      $ adjacencyExample++denseClusterExample = Dense.hierarchicalSpectralCluster+                        SignGroup+                        Nothing+                        Nothing+                        Nothing+                        exampleItems+                        denseAdjacencyExample++denseClusterKExample = Dense.hierarchicalSpectralCluster+                        KMeansGroup+                        (Just 2)+                        Nothing+                        Nothing+                        exampleItems+                        denseAdjacencyExample++-- | Generate the matrix of qgrams from a list of records and qgram length.+exampleEigenMatrix :: Int -> [String] -> E.SparseMatrixXd+exampleEigenMatrix n records = E.fromList (S.nrows mat) (S.ncols mat) . S.toListSM $ mat+  where+    mat = exampleMatrix n records++adjacencyEigenExample :: E.SparseMatrixXd+adjacencyEigenExample = E._imap (\i j v -> if i == j then 0 else v)+                      $ (EF.unB b) * E.transpose (EF.unB b)+  where+    b = EF.getB True $ exampleEigenMatrix 3 exampleData++clusterEigenExample = EF.hierarchicalSpectralCluster+                        SignGroup+                        True+                        Nothing+                        Nothing+                        Nothing+                        exampleItems+                        (Left $ exampleEigenMatrix 3 exampleData)++clusterKEigenExample = EF.hierarchicalSpectralCluster+                        KMeansGroup+                        True+                        (Just 2)+                        Nothing+                        Nothing+                        exampleItems+                        (Left $ exampleEigenMatrix 3 exampleData)++clusterAdjEigenExample = EA.hierarchicalSpectralCluster+                            SignGroup+                            Nothing+                            Nothing+                            Nothing+                            exampleItems+                            adjacencyEigenExample++clusterKAdjEigenExample = EA.hierarchicalSpectralCluster+                        KMeansGroup+                        (Just 2)+                        Nothing+                        Nothing+                        exampleItems+                        adjacencyEigenExample
+ src/Math/Clustering/Hierarchical/Spectral/Types.hs view
@@ -0,0 +1,90 @@+{- Math.Clustering.Hierarchical.Types+Gregory W. Schwartz++Collects the types used in hierarchical clustering.+-}++{-# LANGUAGE BangPatterns #-}+{-# LANGUAGE DeriveGeneric #-}+{-# LANGUAGE StandaloneDeriving #-}++module Math.Clustering.Hierarchical.Spectral.Types+    ( ClusteringTree (..)+    , ClusteringVertex (..)+    , EigenGroup (..)+    , clusteringTreeToDendrogram+    , clusteringTreeToDendrogramCumulative+    , getClusterItemsDend+    , getClusterItemsTree+    , Q (..)+    , NumEigen (..)+    ) where++-- Remote+import Data.Clustering.Hierarchical (Dendrogram (..))+import Data.Monoid ((<>))+import Data.Tree (Tree (..))+import GHC.Generics (Generic)+import Math.Modularity.Types (Q (..))+import Math.TreeFun.Tree (leaves)+import qualified Data.Aeson as A+import qualified Data.Foldable as F+import qualified Data.Vector as V++-- Local++type Items a         = V.Vector a+type ClusteringTree a = Tree (ClusteringVertex a)+type NumEigen = Int++data EigenGroup = SignGroup | KMeansGroup deriving (Read, Show, Generic)++data ClusteringVertex a = ClusteringVertex+    { _clusteringItems  :: !(Items a)+    , _ngMod            :: !Q+    } deriving (Eq, Ord, Read, Show, Generic)++-- | Convert a ClusteringTree to a Dendrogram. Modularity is the distance.+clusteringTreeToDendrogram :: ClusteringTree a -> Dendrogram (Items a)+clusteringTreeToDendrogram = go+  where+    go (Node { rootLabel = !n, subForest = []}) = Leaf $ _clusteringItems n+    go (Node { rootLabel = !n, subForest = [x, y]}) =+        Branch (unQ . _ngMod $ n) (go x) (go y)+    go (Node { subForest = xs}) =+        error $ "Clustering tree has "+            <> (show $ length xs)+            <> " children. Requires two or none."++-- | Convert a ClusteringTree to a Dendrogram. Modularity is the distance, such+-- that the distance is the modularity plus the maximum distance of each branch.+clusteringTreeToDendrogramCumulative :: ClusteringTree a -> Dendrogram (Items a)+clusteringTreeToDendrogramCumulative = fst . go+  where+    go (Node { rootLabel = !n, subForest = []}) =+        (Leaf (_clusteringItems n), 0)+    go (Node { rootLabel = !n, subForest = [x, y]}) =+        (Branch newD l r, newD)+      where+        newD = (unQ . _ngMod $ n) + max lDist rDist+        (!l, !lDist) = go x+        (!r, !rDist) = go y+    go (Node { subForest = xs}) =+        error $ "Clustering tree has "+            <> (show $ length xs)+            <> " children. Requires two or none."++-- | Gather clusters (leaves) from the dendrogram.+getClusterItemsDend :: Foldable t => t (Items a) -> [Items a]+getClusterItemsDend = F.toList++-- | Gather clusters (leaves) from the tree.+getClusterItemsTree :: ClusteringTree a -> [Items a]+getClusterItemsTree = fmap _clusteringItems . leaves++deriving instance (Read a) => Read (Dendrogram a)+deriving instance Generic (Dendrogram a)++instance (A.ToJSON a) => A.ToJSON (Dendrogram a) where+    toEncoding = A.genericToEncoding A.defaultOptions+instance (A.FromJSON a) => A.FromJSON (Dendrogram a)
+ src/Math/Clustering/Hierarchical/Spectral/Utility.hs view
@@ -0,0 +1,28 @@+{- Math.Clustering.Hierarchical.Spectral.Utility+Gregory W. Schwartz++Collects utility functions for the clustering section of the program.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Clustering.Hierarchical.Spectral.Utility+  ( subsetVector+  ) where++-- Remote+import Data.Maybe (fromMaybe)+import qualified Data.Foldable as F+import qualified Data.Vector as V++-- Local+++subsetVector :: V.Vector a -> [Int] -> V.Vector a+subsetVector xs =+    V.fromList+        . F.foldr' (\ !i !acc+                    -> ( fromMaybe (error "Out of bounds in subsetVector.")+                      $ xs V.!? i+                      ) : acc+                    ) []
+ src/Math/Graph/Components.hs view
@@ -0,0 +1,37 @@+{- Math.Graph.Components+Gregory W. Schwartz++Find connected components of matrices.+-}++{-# LANGUAGE BangPatterns #-}++module Math.Graph.Components+  ( getComponentMats+  , getComponentMatsItems+  ) where++-- Remote+import Data.List (sort)+import qualified Data.Graph.Inductive as G+import qualified Data.Vector as V++-- Local+import Math.Graph.Types+import Math.Clustering.Hierarchical.Spectral.Utility++-- | Get the components of a graphable object, an adjacency matrix.+getComponentMats :: (Graphable a) => a -> [a]+getComponentMats mat = fmap (fromGraph . flip G.subgraph gr) . G.components $ gr+  where+    gr = toGraph mat++-- | Get the components of a graphable object, an adjacency matrix with the+-- associated items.+getComponentMatsItems :: (Graphable b) => V.Vector a -> b -> [(V.Vector a, b)]+getComponentMatsItems items mat =+    fmap (\ !xs -> (subsetVector items . sort $ xs, fromGraph . G.subgraph xs $ gr))+      . G.components+      $ gr+  where+    gr = toGraph mat
+ src/Math/Graph/Types.hs view
@@ -0,0 +1,71 @@+{- Math.Graph.Types+Gregory W. Schwartz++Types used for graph sections of the program.+-}++{-# LANGUAGE BangPatterns #-}+{-# LANGUAGE TypeSynonymInstances #-}+{-# LANGUAGE FlexibleInstances #-}++module Math.Graph.Types where++-- Remote+import Data.List (sort)+import qualified Data.Eigen.SparseMatrix as E+import qualified Data.Map.Strict as Map+import qualified Data.Graph.Inductive as G+import qualified Data.Sparse.Common as S+import qualified Data.Vector.Unboxed as V+import qualified Math.Clustering.Spectral.Dense as D+import qualified Numeric.LinearAlgebra as H+import qualified Numeric.LinearAlgebra.Sparse as S++-- Local+import qualified Math.Clustering.Spectral.Dense as D+import qualified Math.Clustering.Spectral.Eigen.AdjacencyMatrix as E+import qualified Math.Clustering.Spectral.Sparse as S++-- | Get a re-mapped edge list with nodes ordered from 0 to the number of nodes+-- in the graph.+orderedEdges :: G.Gr Int a -> [(Int, Int, a)]+orderedEdges gr =+    fmap (\(!i,  !j,  !v) -> (getUpdate i, getUpdate j, v)) . G.labEdges $ gr+  where+    nodeMap = Map.fromList . flip zip [0..] . sort . G.nodes $ gr+    getUpdate = flip ( Map.findWithDefault+                        (error "Unexpected missing node in orderedEdges.")+                     )+                     nodeMap++-- | Graphable class for converting matrices to and from a graph.+class Graphable a where+  toGraph :: a -> G.Gr Int Double+  fromGraph :: G.Gr Int Double -> a++instance Graphable D.AdjacencyMatrix where+  toGraph mat = G.mkGraph (zip [0 .. H.rows mat - 1] [0 .. H.rows mat - 1])+              . filter (\(_, _, x) -> x /= 0)+              . concatMap (\(!i, !xs) -> fmap (\(!j, !v) -> (i, j, v)) xs)+              . zip [0..]+              . fmap (zip [0..] . H.toList)+              . H.toRows+              $ mat+  fromGraph gr = H.assoc (G.noNodes gr, G.noNodes gr) 0+               . fmap (\(!i, !j, !v) -> ((i, j), v))+               . orderedEdges+               $ gr++instance Graphable S.AdjacencyMatrix where+  toGraph mat = G.mkGraph (zip [0 .. S.nrows mat - 1] [0 .. S.nrows mat - 1])+              . filter (\(_, _, x) -> x /= 0)+              . S.toListSM+              $ mat+  fromGraph gr = S.fromListSM (G.noNodes gr, G.noNodes gr) . orderedEdges $ gr++instance Graphable E.AdjacencyMatrix where+  toGraph mat = G.mkGraph (zip [0 .. E.rows mat - 1] [0 .. E.rows mat - 1])+              . filter (\(_, _, x) -> x /= 0)+              . E.toList+              $ mat+  fromGraph gr = E.fromList (G.noNodes gr) (G.noNodes gr) . orderedEdges $ gr