------------------------------------------------------------------------
-- |
  Module       :   Data . Datamining . Clustering . SGM3
  Copyright    :   ( c ) 2012 - 2022   
-- License     :  BSD-style
-- Maintainer  :  
-- Stability   :  experimental
-- Portability :  portable
--
  A Self - generating Model ( SGM ) . An SGM maps input patterns
-- onto a set, where each element in the set is a model of the input
  data . An SGM is like a     Map ( SOM ) , except :
--
-- * 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.
-- * New models are created on-the-fly when no existing model is
    similar enough to an input pattern . If the SGM is at capacity ,
--   the least useful model will be deleted.
--
-- This implementation supports the use of non-numeric patterns.
--
  In layman 's terms , a SGM can be useful when you you want to build
-- a set of models on some data. A tutorial is available at
-- <>.
--
-- References:
--
  *    ,   , and   .
--   The Self-generating Model: an Adaptation of the Self-organizing Map
    for Intelligent Agents and Data Mining .
    In : Artificial Life and Intelligent Agents : Second International
    Symposium , ALIA 2016 , Birmingham , UK , June 14 - 15 , 2016 ,
    Revised Selected Papers .
     . by    et al . Springer International Publishing ,
    2018 , pp . 59–72 .
--   Available at /.
--
  *    ,   , and   .
     : A pattern - seeking artificial life species .
    Artificial Life , ( 18)4:399–423 , 2012 .
--   Available at /.
--
  *  , T. ( 1982 ) . Self - organized formation of topologically
    correct feature maps . Biological Cybernetics , 43 ( 1 ) , 59–69 .
------------------------------------------------------------------------

module Data.Datamining.Clustering.SGM3
  (
    -- * Construction
    SGM(..),
    makeSGM,
    -- * Deconstruction
    time,
    isEmpty,
    size,
    modelMap,
    counterMap,
    modelAt,
    -- * Learning and classification
    exponential,
    classify,
    trainAndClassify,
    train,
    trainBatch
  ) where

import           Data.Datamining.Clustering.SGM3Internal


----------------------------------------------------------------------
 |
 License     :  BSD-style
 Maintainer  :  
 Stability   :  experimental
 Portability :  portable

 onto a set, where each element in the set is a model of the input

 * 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.
 * New models are created on-the-fly when no existing model is
   the least useful model will be deleted.

 This implementation supports the use of non-numeric patterns.

 a set of models on some data. A tutorial is available at
 <>.

 References:

   The Self-generating Model: an Adaptation of the Self-organizing Map
   Available at /.

   Available at /.

----------------------------------------------------------------------
 * Construction
 * Deconstruction
 * Learning and classification
  Module       :   Data . Datamining . Clustering . SGM3
  Copyright    :   ( c ) 2012 - 2022   
  A Self - generating Model ( SGM ) . An SGM maps input patterns
  data . An SGM is like a     Map ( SOM ) , except :
    similar enough to an input pattern . If the SGM is at capacity ,
  In layman 's terms , a SGM can be useful when you you want to build
  *    ,   , and   .
    for Intelligent Agents and Data Mining .
    In : Artificial Life and Intelligent Agents : Second International
    Symposium , ALIA 2016 , Birmingham , UK , June 14 - 15 , 2016 ,
    Revised Selected Papers .
     . by    et al . Springer International Publishing ,
    2018 , pp . 59–72 .
  *    ,   , and   .
     : A pattern - seeking artificial life species .
    Artificial Life , ( 18)4:399–423 , 2012 .
  *  , T. ( 1982 ) . Self - organized formation of topologically
    correct feature maps . Biological Cybernetics , 43 ( 1 ) , 59–69 .

module Data.Datamining.Clustering.SGM3
  (
    SGM(..),
    makeSGM,
    time,
    isEmpty,
    size,
    modelMap,
    counterMap,
    modelAt,
    exponential,
    classify,
    trainAndClassify,
    train,
    trainBatch
  ) where

import           Data.Datamining.Clustering.SGM3Internal

