som 4.2 → 5.0

raw patch · 4 files changed

+30/−30 lines, 4 filesPVP ok

version bump matches the API change (PVP)

API changes (from Hackage documentation)

Files

• som.cabal +1/−1
• src/Data/Datamining/Clustering/Classifier.hs +4/−3
• src/Data/Datamining/Clustering/SOM.hs +19/−8
• src/Data/Datamining/Clustering/SOMInternal.hs +6/−18

@@ -1,5 +1,5 @@ name:           som-version:        4.2+version:        5.0 synopsis:       Self-Organising Maps description:    A Kohonen Self-organising Map (SOM) maps input patterns                  onto a regular grid (usually two-dimensional) where each

@@ -34,8 +34,8 @@   models ∷ c k p → [p]    -- | @'differences' c target@ returns the indices of all nodes in -  --   @c@, paired with the difference between @target@ and the node's -  --   model.+  --   @c@, paired with the difference between @target@ and the +  --   node's model.   differences ∷ (Pattern p, v ~ Metric p) ⇒ c k p → p → [(k, v)]    -- | @classify c target@ returns the index of the node in @c@ @@ -59,7 +59,8 @@   --   index of the node in @c@ whose model best matches the input   --   @target@, and a modified copy of the classifier @c@ that has   --   partially learned the @target@. Invoking @classifyAndTrain c p@-  --   may be faster than invoking @(p `classify` c, train c p)@, but they+  --   may be faster than invoking @(p `classify` c, train c p)@, but +  --   they   --   should give identical results.   classifyAndTrain      ∷ (Ord v, v ~ Metric p) ⇒ 

@@ -7,17 +7,29 @@ -- Stability   :  experimental -- Portability :  portable ----- A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto a --- regular grid (usually two-dimensional) where each node in the grid is--- a model of the input data, and does so using a method which ensures --- that any topological relationships within the input data are also --- represented in the grid. This implementation supports the use of --- non-numeric patterns.+-- A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto+-- a regular grid (usually two-dimensional) where each node in the grid+-- is a model of the input data, and does so using a method which+-- ensures that any topological relationships within the input data are+-- also represented in the grid. This implementation supports the use+-- of non-numeric patterns. -- -- In layman's terms, a SOM can be useful when you you want to discover -- the underlying structure of some data. A tutorial is available at -- <https://github.com/mhwombat/som/wiki>. --+-- NOTES: +--+-- * Version 5.0 fixed a bug in the @`decayingGaussian`@ function. If+--   you use @`defaultSOM`@ (which uses this function), your SOM+--   should now learn more quickly.+--+-- * The @gaussian@ function has been removed because it is not as+--   useful for SOMs as I originally thought. It was originally designed+--   to be used as a factor in a learning function. However, in most+--   cases the user will want to introduce a time decay into the+--   exponent, rather than simply multiply by a factor.+-- -- References: -- -- * Kohonen, T. (1982). Self-organized formation of topologically @@ -32,7 +44,6 @@     SOM,     defaultSOM,     customSOM,-    gaussian,     decayingGaussian,     -- * Deconstruction     toGridMap,@@ -42,6 +53,6 @@   ) where  import Data.Datamining.Clustering.SOMInternal (SOM, defaultSOM,-  customSOM, gaussian, decayingGaussian, toGridMap, trainNeighbourhood,+  customSOM, decayingGaussian, toGridMap, trainNeighbourhood,   incrementCounter) 

@@ -20,7 +20,6 @@     SOM(..),     defaultSOM,     customSOM,-    gaussian,     decayingGaussian,     -- * Deconstruction     toGridMap,@@ -149,7 +148,7 @@ --   at "time" zero. The BMU is the model which best matches the --   current target pattern. -----   [@w@] The width of the bell curve at "time" zero.+--   [@w@] The width of the bell curve at time zero. -- --   [@t@] Controls how rapidly the learning rate decays. After this --   time, any learning done by the classifier will be negligible.@@ -190,19 +189,6 @@         sCounter=0       } --- | Calculates @r/e/^(-d^2/2w^2)@.---   This form of the Gaussian function is useful as a learning rate---   function. In @'gaussian' r w d@, @r@ specifies the highest learning---   rate, which will be applied to the SOM node that best matches the---   input pattern. The learning rate applied to other nodes will be---   applied based on their distance @d@ from the best matching node.---   The value @w@ controls the \'width\' of the Gaussian. Higher values---   of @w@ cause the learning rate to fall off more slowly with---   distance @d@.-gaussian ∷ Floating a ⇒ a → a → Int → a-gaussian r w d = r * exp (-d'*d'/(2*w*w))-  where d' = fromIntegral d- -- | Configures a typical learning function for classifiers. --   @'decayingGaussian' r w0 tMax@ returns a bell curve-shaped --   function. At time zero, the maximum learning rate (applied to the@@ -211,6 +197,8 @@ --   @tMax@, the learning rate is negligible. decayingGaussian   ∷ Floating a ⇒ a → a → Int → (Int → Int → a)-decayingGaussian r w0 tMax =-  \t d → let t' = fromIntegral t in gaussian r w0 d * exp (-t'/tMax')-  where tMax' = fromIntegral tMax+decayingGaussian r w0 tMax t d = r * s * exp (-(d'*d')/(2*w0*w0*s*s))+    where s = exp (-t'/tMax')+          t' = fromIntegral t+          tMax' = fromIntegral tMax+          d' = fromIntegral d