som 7.2.4 → 7.3.0

raw patch · 4 files changed

+172/−2 lines, 4 filesdep +containersPVP ok

version bump matches the API change (PVP)

Dependencies added: containers

API changes (from Hackage documentation)

Files

• som.cabal +6/−2
• src/Data/Datamining/Clustering/SSOM.hs +46/−0
• src/Data/Datamining/Clustering/SSOMInternal.hs +118/−0
• test/Main.hs +2/−0

@@ -1,5 +1,5 @@ Name:              som-Version:           7.2.4+Version:           7.3.0 Stability:         experimental Synopsis:          Self-Organising Maps. Description:       A Kohonen Self-organising Map (SOM) maps input patterns @@ -33,12 +33,13 @@ source-repository this   type:     git   location: https://github.com/mhwombat/som.git-  tag:      7.2.4+  tag:      7.3.0   library   hs-source-dirs:  src   build-depends:   base ==4.*,+                   containers ==0.5.*,                    grid ==7.*,                    MonadRandom ==0.3.*   ghc-options:     -Wall@@ -46,6 +47,8 @@                    Data.Datamining.Clustering.SOMInternal,                    Data.Datamining.Clustering.DSOM,                    Data.Datamining.Clustering.DSOMInternal,+                   Data.Datamining.Clustering.SSOM,+                   Data.Datamining.Clustering.SSOMInternal,                    Data.Datamining.Clustering.Classifier,                    Data.Datamining.Pattern @@ -56,6 +59,7 @@                    QuickCheck ==2.7.*,                    test-framework ==0.8.*,                    som,+                   containers ==0.5.*,                    grid ==7.*,                    MonadRandom ==0.3.*,                    random ==1.1.*

@@ -0,0 +1,46 @@+------------------------------------------------------------------------+-- |+-- Module      :  Data.Datamining.Clustering.SSOM+-- Copyright   :  (c) Amy de Buitléir 2012-2014+-- License     :  BSD-style+-- Maintainer  :  amy@nualeargais.ie+-- Stability   :  experimental+-- Portability :  portable+--+-- A Simplified Self-organising Map (SSOM). An SSOM maps input patterns+-- onto a set, where each element in the set is a model of the input+-- data. An SSOM is like a Kohonen Self-organising Map (SOM), except+-- that instead of a grid, it uses a simple set of unconnected models.+-- Since the models are unconnected, only the model that best matches+-- the input is ever updated. This makes it faster, however,+-- topological relationships within the input data are not preserved.+-- This implementation supports the use of non-numeric patterns.+--+-- In layman's terms, a SSOM can be useful when you you want to build+-- a set of models on some data. A tutorial is available at+-- <https://github.com/mhwombat/som/wiki>.+--+-- References:+--+-- * de Buitléir, Amy, Russell, Michael and Daly, Mark. (2012). Wains:+--   A pattern-seeking artificial life species. Artificial Life, 18 (4),+--   399-423. +-- +-- * Kohonen, T. (1982). Self-organized formation of topologically +--   correct feature maps. Biological Cybernetics, 43 (1), 59–69.+------------------------------------------------------------------------++module Data.Datamining.Clustering.SSOM+  (+    -- * Construction+    SSOM(..),+    Gaussian(..),+    -- * Deconstruction+    toMap,+    -- * Advanced control+    trainNode,+  ) where++import Data.Datamining.Clustering.SSOMInternal (SSOM(..),+  Gaussian(..), toMap, trainNode)+

@@ -0,0 +1,118 @@+------------------------------------------------------------------------+-- |+-- Module      :  Data.Datamining.Clustering.SSOMInternal+-- Copyright   :  (c) Amy de Buitléir 2012-2014+-- License     :  BSD-style+-- Maintainer  :  amy@nualeargais.ie+-- Stability   :  experimental+-- Portability :  portable+--+-- A module containing private @SSOM@ internals. Most developers should+-- use @SSOM@ instead. This module is subject to change without notice.+--+------------------------------------------------------------------------+{-# LANGUAGE TypeFamilies, FlexibleContexts, FlexibleInstances,+    MultiParamTypeClasses, DeriveGeneric #-}++module Data.Datamining.Clustering.SSOMInternal where++import Data.List (foldl', minimumBy)+import Data.Ord (comparing)+import Data.Datamining.Pattern (Pattern(..))+import Data.Datamining.Clustering.Classifier(Classifier(..))+import qualified Data.Map.Strict as M+import GHC.Generics (Generic)+import Prelude hiding (lookup)++-- | A function used to adjust the models in a classifier.+class LearningFunction f where+  type LearningRate f+  -- | @'rate' f t@ returns the learning rate for a node.+  --   The parameter @f@ is the learning function.+  --   The parameter @t@ indicates how many patterns (or pattern+  --   batches) have previously been presented to the classifier.+  --   Typically this is used to make the learning rate decay over time.+  --   The output is the learning rate for that node (the amount by+  --   which the node's model should be updated to match the target).+  --   The learning rate should be between zero and one.+  rate :: f -> LearningRate f -> LearningRate f++-- | A typical learning function for classifiers.+--   @'Gaussian' 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:+--+--   * 0 < rf << r0 < 1+--+--   * 0 < tf+--+--   where << means "is much smaller than" (not the Haskell @<<@+--   operator!)+data Gaussian a = Gaussian 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))++-- | A Simplified Self-Organising Map (SSOM).+data SSOM f t k p = SSOM+  {+    -- | Maps patterns to nodes.+    sMap :: M.Map k p,+    -- | The function used to update the nodes.+    learningFunction :: f,+    -- | A counter used as a "time" parameter.+    --   If you create the SSOM with a counter value @0@, and don't+    --   directly modify it, then the counter will represent the number+    --   of patterns that this SSOM has classified.+    counter :: t+  } deriving (Eq, Show, Generic)++-- | Extracts the current models from the SSOM.+--   A synonym for @'sMap'@.+toMap :: SSOM f t k p -> M.Map k p+toMap = sMap++-- | Trains the specified node and the neighbourood around it to better+--   match a target.+--   Most users should use @train@, which automatically determines+--   the BMU and trains it and its neighbourhood.+trainNode+  :: (Pattern p, LearningFunction f, Metric p ~ LearningRate f,+    Num (LearningRate f), Ord k, Integral t)+      => SSOM f t k p -> k -> p -> SSOM f t k p+trainNode s k target = s { sMap=gm' }+  where gm = sMap s+        gm' = M.adjust (makeSimilar target r) k gm+        r = rate (learningFunction s) (fromIntegral $ counter s)++incrementCounter :: Num t => SSOM f t k p -> SSOM f t k p+incrementCounter s = s { counter=counter s + 1}++justTrain+  :: (Ord (Metric p), Pattern p, LearningFunction f,+    Metric p ~ LearningRate f, Num (LearningRate f), Ord k, Integral t)+      => SSOM f t k p -> p -> SSOM f t k p+justTrain s p = trainNode s bmu p+  where ds = M.toList . M.map (p `difference`) . toMap $ s+        bmu = f ds+        f [] = error "SSOM has no models"+        f xs = fst $ minimumBy (comparing snd) xs++instance+  (Pattern p, Ord (Metric p), LearningFunction f,+    Metric p ~ LearningRate f, Num (LearningRate f), Ord k, Integral t)+      => Classifier (SSOM f t) k p where+  toList = M.toList . toMap+  -- TODO: If the # of models is fixed, make more efficient+  numModels = length . M.keys . sMap+  models = M.elems . toMap+  differences s p = M.toList . M.map (p `difference`) $ toMap s+  trainBatch s = incrementCounter . foldl' justTrain s+  reportAndTrain s p = (bmu, ds, s')+    where ds = differences s p+          bmu = fst $ minimumBy (comparing snd) ds+          s' = incrementCounter . trainNode s bmu $ p

@@ -15,6 +15,7 @@  import Data.Datamining.PatternQC ( test ) import Data.Datamining.Clustering.SOMQC ( test )+import Data.Datamining.Clustering.SSOMQC ( test ) import Data.Datamining.Clustering.DSOMQC ( test )  import Test.Framework as TF ( defaultMain, Test )@@ -23,6 +24,7 @@ tests =    [      Data.Datamining.PatternQC.test,+    Data.Datamining.Clustering.SSOMQC.test,     Data.Datamining.Clustering.SOMQC.test,     Data.Datamining.Clustering.DSOMQC.test   ]