som 7.3.1 → 7.4.0

raw patch · 4 files changed

+11/−11 lines, 4 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• som.cabal +2/−2
• src/Data/Datamining/Clustering/SOMInternal.hs +1/−1
• src/Data/Datamining/Clustering/SSOM.hs +2/−2
• src/Data/Datamining/Clustering/SSOMInternal.hs +6/−6

@@ -1,5 +1,5 @@ Name:              som-Version:           7.3.1+Version:           7.4.0 Stability:         experimental Synopsis:          Self-Organising Maps. Description:       A Kohonen Self-organising Map (SOM) maps input patterns @@ -33,7 +33,7 @@ source-repository this   type:     git   location: https://github.com/mhwombat/som.git-  tag:      7.3.1+  tag:      7.4.0   library

@@ -159,7 +159,7 @@  -- | Trains the specified node and the neighbourood around it to better --   match a target.---   Most users should use @train@, which automatically determines+--   Most users should use @'train'@, which automatically determines --   the BMU and trains it and its neighbourhood. trainNeighbourhood   :: (Pattern p, G.Grid (gm p), GM.GridMap gm p,

@@ -34,7 +34,7 @@   (     -- * Construction     SSOM(..),-    Gaussian(..),+    Exponential(..),     -- * Deconstruction     toMap,     -- * Advanced control@@ -42,5 +42,5 @@   ) where  import Data.Datamining.Clustering.SSOMInternal (SSOM(..),-  Gaussian(..), toMap, trainNode)+  Exponential(..), toMap, trainNode) 

@@ -38,7 +38,7 @@   rate :: f -> LearningRate f -> LearningRate f  -- | A typical learning function for classifiers.---   @'Gaussian' r0 rf tf@ returns a gaussian function. At time zero,+--   @'Exponential' r0 rf tf@ returns a gaussian function. At time zero, --   the learning rate is @r0@. Over time the learning rate tapers off, --   until at time @tf@, the learning rate is @rf@. Normally the --   parameters should be chosen such that:@@ -49,13 +49,13 @@ -- --   where << means "is much smaller than" (not the Haskell @<<@ --   operator!)-data Gaussian a = Gaussian a a a+data Exponential a = Exponential a a a   deriving (Eq, Show, Generic)  instance (Floating a, Fractional a, Num a)-    => LearningFunction (Gaussian a) where-  type LearningRate (Gaussian a) = a-  rate (Gaussian r0 rf tf) t = r0 * ((rf/r0)**(t/tf))+    => LearningFunction (Exponential a) where+  type LearningRate (Exponential a) = a+  rate (Exponential r0 rf tf) t = r0 * ((rf/r0)**(t/tf))  -- | A Simplified Self-Organising Map (SSOM). data SSOM f t k p = SSOM@@ -77,7 +77,7 @@ toMap = sMap  -- | Trains the specified node to better match a target.---   Most users should use @train@, which automatically determines+--   Most users should use @'train'@, which automatically determines --   the BMU and trains it. trainNode   :: (Pattern p, LearningFunction f, Metric p ~ LearningRate f,