[TextFlow] Type rename

src/Gargantext/Core/Text/Examples.hs

@@ -113,7 +113,7 @@ ex_cooc = cooc <$> ex_terms
 --     0.0,  1.0,  1.0,  0.5,
 --     0.0,  0.0,  1.0,  0.5,
 --     0.0,  0.0,  0.0,  1.0],(Vector (Z :. 4) [0.5833333333333334,0.5833333333333334,0.75,0.5833333333333334],Vector (Z :. 4) [-0.5833333333333334,-0.4166666666666667,0.41666666666666674,0.5833333333333334]))
-ex_cooc_mat :: IO (Map Label Index, Matrix Int, Matrix Double, (DAA.Vector InclusionExclusion, DAA.Vector SpecificityGenericity))
+ex_cooc_mat :: IO (Map Label Index, Matrix Int, Matrix Double, (DAA.Vector GenericityInclusion, DAA.Vector SpecificityExclusion))
 ex_cooc_mat = do
   m <- ex_cooc
   let (ti,_) = createIndices m

src/Gargantext/Core/Text/List.hs

@@ -140,7 +140,10 @@ buildNgramsTermsList l n m s uCid mCid = do
 
     -- Get Local Scores now for selected grouped ngrams
     selectedTerms = Set.toList $ List.foldl'
-                      (\set (GroupedText _ l _ g _ _) -> Set.union set $ Set.union g $ Set.singleton l)
+                      (\set (GroupedText _ l _ g _ _) -> Set.union set
+                                                       $ Set.union g
+                                                       $ Set.singleton l
+                      )
                       Set.empty
                       (groupedMonoHead <> groupedMultHead)
 

src/Gargantext/Core/Text/Metrics.hs

@@ -57,8 +57,8 @@ scored = map2scored . (pcaReduceTo (Dimension 2)) . scored2map
 -- TODO change type with (x,y)
 data Scored ts = Scored
   { _scored_terms  :: !ts
-  , _scored_incExc :: !InclusionExclusion
-  , _scored_speGen :: !SpecificityGenericity
+  , _scored_incExc :: !GenericityInclusion
+  , _scored_speGen :: !SpecificityExclusion
   } deriving (Show)
 
 localMetrics' :: Ord t => Map (t,t) Int -> Map t (Vec.Vector Double)
@@ -76,7 +76,7 @@ localMetrics' m = Map.fromList $ zipWith (\(_,t) (inc,spe) -> (t, Vec.fromList [
 -- TODO in the textflow we end up needing these indices , it might be
 -- better to compute them earlier and pass them around.
 scored' :: Ord t => Map (t,t) Int -> [Scored t]
-scored' m = zipWith (\(_,t) (inc,spe) -> Scored t (inc) (spe)) (Map.toList fi) scores
+scored' m = zipWith (\(_,t) (inc,spe) -> Scored t inc spe) (Map.toList fi) scores
   where
     (ti, fi) = createIndices m
     (is, ss) = incExcSpeGen $ cooc2mat ti m

src/Gargantext/Core/Viz/Graph/Distances/Matrice.hs

@@ -196,7 +196,7 @@ measureConditional m = run $ matProba (dim m)
 -- in the corpus and _[n_{ij}\] the number of its occurrences we get:
 --
 -- \[P_c=max(\frac{n_i}{n_{ij}},\frac{n_j}{n_{ij}} )\]
-conditional' :: Matrix Int -> (Matrix InclusionExclusion, Matrix SpecificityGenericity)
+conditional' :: Matrix Int -> (Matrix GenericityInclusion, Matrix SpecificityExclusion)
 conditional' m = ( run $ ie $ map fromIntegral $ use m
                  , run $ sg $ map fromIntegral $ use m
                  )
@@ -450,15 +450,15 @@ Jean-Philippe Cointet (CREA, TSV), David Chavalarias (CREA) (Submitted
 on 15 Mar 2008), Networks and Heterogeneous Media 3, 2 (2008) 267 - 276,
 arXiv:0803.2315 [cs.OH]
 -}
-type InclusionExclusion    = Double
-type SpecificityGenericity = Double
+type GenericityInclusion    = Double
+type SpecificityExclusion = Double
 
 data SquareMatrix      = SymetricMatrix | NonSymetricMatrix
 type SymetricMatrix    = Matrix
 type NonSymetricMatrix = Matrix
 
 
-incExcSpeGen :: Matrix Int -> (Vector InclusionExclusion, Vector SpecificityGenericity)
+incExcSpeGen :: Matrix Int -> (Vector GenericityInclusion, Vector SpecificityExclusion)
 incExcSpeGen m = (run' inclusionExclusion m, run' specificityGenericity m)
   where
     run' fun mat = run $ fun $ map fromIntegral $ use mat