[TESTS] ready for optim

app/Main.hs

-{-# LANGUAGE NoImplicitPrelude   #-}
+{-# LANGUAGE NoImplicitPrelude       #-}
 
 module Main where
 
-import Graph.Tools
-import Graph.BAC.Clustering
-import System.Environment (getArgs)
+import Data.Set as Set
+import Data.String.Conversions (cs)
+import Data.Text (Text)
+import Graph.BAC.ProxemyOptim
+import Graph.BAC.ProxemyOptim
+import Graph.FGL
+import Prelude (String)
 import Protolude
+import qualified Data.IntMap                       as Dict
+import qualified Data.List as List
+import qualified Eigen.Matrix as Matrix
+import qualified Eigen.SparseMatrix as SMatrix
+import qualified Prelude as Prelude
 
 
 main :: IO ()
 main = do
-  [fp]    <- getArgs
-  graph <- readFileGraph TestGraph fp
-  print (clusterGraph 3 graph)
-
-  
-
+  m <- randomAdjacency
+  let
+    g :: Graph () ()
+    g = mkGraphUfromEdges
+      $ List.map (\(x,y,_) -> (x,y))
+      $ SMatrix.toList m
+    result = withG g (identity  . (clusteringOptim 3 Conf))
+  print (result)

app/Main2.hs

+{-# LANGUAGE NoImplicitPrelude   #-}
+
+module Main2 where
+
+import Graph.Tools
+import Graph.BAC.Clustering
+import System.Environment (getArgs)
+import Protolude
+
+
+main :: IO ()
+main = do
+  [fp]    <- getArgs
+  graph <- readFileGraph TestGraph fp
+  print (clusterGraph 3 graph)
+
+  
+

gargantext-graph.cabal

@@ -4,7 +4,7 @@ cabal-version: 1.12
 --
 -- see: https://github.com/sol/hpack
 --
--- hash: 0738cfbe36f67275fe7fcd289fd26bddd3727957358ad06543242f719c4f34f1
+-- hash: 4cecc6b992a2dda8a33d66136d8ffcda1eb4fcfed9fb2a21df529821278d524d
 
 name:           gargantext-graph
 version:        0.1.0.0
@@ -68,6 +68,7 @@ library
 executable gargantext-graph-exe
   main-is: Main.hs
   other-modules:
+      Main2
       Paths_gargantext_graph
   hs-source-dirs:
       app

install

 #!/bin/bash
-# 
-stack install --profile # --test
+
+stack install --profile # --test # --haddock

package.yaml

@@ -58,6 +58,8 @@ executables:
     - -rtsopts
     - -with-rtsopts=-N
     - -fprof-auto
+      # - -Wmissing-signatures
+      # - -Wcompat
     dependencies:
     - gargantext-graph
 

run

 #!/bin/bash
 
-~/.local/bin/gargantext-graph-exe $1 +RTS -p # -sstderr
+#~/.local/bin/gargantext-graph-exe $1 +RTS -p # -sstderr
+time ~/.local/bin/gargantext-graph-exe +RTS -p # -sstderr

src/Graph/BAC/ProxemyOptim.hs

@@ -97,13 +97,13 @@ data ClusteringMode = Part | Over | Both
 type StrictClustering   a = Clustering a
 type OverlapsClustering a = Clustering a
 
-data Clustering a = ClusteringIs { parts :: Dict (Set a)
-                                 , index :: Dict Int
-                                 , score :: Double
-                                 , mode  :: ClusteringMode
+data Clustering a = ClusteringIs { parts :: !(Dict (Set a))
+                                 , index :: !(Dict Int)
+                                 , score :: !(Double)
+                                 , mode  :: !ClusteringMode
                                  }
-                  | Clusterings { strict :: StrictClustering   a
-                                , over   :: OverlapsClustering a
+                  | Clusterings { strict :: !(StrictClustering   a)
+                                , over   :: !(OverlapsClustering a)
                                 }
   deriving (Show, Eq)
 ---------------------------------------------------------------
@@ -122,8 +122,8 @@ type MatrixS          n = SparseMatrix n n Double
 
 
 data Similarity = Conf | Mod
-data SimilarityMatrix n = SimConf (ConfluenceMatrix n)
-                        | SimMod  (ModularityMatrix n)
+data SimilarityMatrix n = SimConf !(ConfluenceMatrix n)
+                        | SimMod  !(ModularityMatrix n)
 
 type Dict = IntMap
 
@@ -460,5 +460,5 @@ randomAdjacency = do
   m1 <- randomMatrix
   m2 <- randomMatrix
   pure $ SMatrix.fromMatrix
-       $ DMatrix.imap (\i j v -> if i < j && v > 0.5 then 1 else 0)
+       $ DMatrix.imap (\i j v -> if i < j && v > 0.9 then 1 else 0)
        $ DMatrix.mul m1 m2

test/Spec.hs

 
 {-# LANGUAGE NoImplicitPrelude       #-}
 
+module Spec where
+
 import Data.Set as Set
 import Data.String.Conversions (cs)
 import Data.Text (Text)
@@ -15,25 +17,37 @@ import qualified Eigen.SparseMatrix as SMatrix
 import qualified Prelude as Prelude
 import qualified Data.List as List
 
-edges_test :: [(Int,Int)]
-edges_test=[(0,1),(0,2),(0,4),(0,5),(0,3),(0,6)
-           ,(1,2),(1,3),(2,3),(4,5),(4,6),(5,6)
-           ,(7,8),(7,3),(7,4),(8,2),(8,5)
-           ]
-
 
-main_test :: IO ()
-main_test = do
+main :: IO ()
+main = do
   let 
+
+      edges_test :: [(Int,Int)]
+      edges_test=[(0,1),(0,2),(0,4),(0,5),(0,3),(0,6)
+                 ,(1,2),(1,3),(2,3),(4,5),(4,6),(5,6)
+                 ,(7,8),(7,3),(7,4),(8,2),(8,5)
+                 ]
+
+      clustering_result =
+        ClusteringIs { parts = Dict.fromList [(0, Set.fromList [0,4,5,6])
+                                             ,(1, Set.fromList [1,2,3])
+                                             ,(7, Set.fromList [7,8])]
+                     , index = Dict.fromList [(0,0),(1,1),(2,1)
+                                             ,(3,1),(4,0),(5,0)
+                                             ,(6,0),(7,7),(8,7)
+                                             ]
+                     , score = 3.0558391780792453
+                     , mode = Part
+                     }
+
       g :: Graph () ()
       g = mkGraphUfromEdges edges_test
 
       result = withG g (identity  . (clusteringOptim 3 Conf))
-      test   = ClusteringIs { parts = Dict.fromList [(0, Set.fromList [0,4,5,6]),(1, Set.fromList [1,2,3]),(7, Set.fromList [7,8])], index = Dict.fromList [(0,0),(1,1),(2,1),(3,1),(4,0),(5,0),(6,0),(7,7),(8,7)], score = 3.0558391780792453, mode = Part}
-
-  print $ result == test
 
+  print $ result == clustering_result
 
+test_random :: IO ()
 test_random = do
   m <- randomAdjacency
   let