# Author::    Sergio Fierens (implementation)
# License::   MPL 1.1
# Project::   ai4r
# Url::       http://ai4r.rubyforge.org/
#
# You can redistribute it and/or modify it under the terms of 
# the Mozilla Public License version 1.1  as published by the 
# Mozilla Foundation at http://www.mozilla.org/MPL/MPL-1.1.txt

require File.dirname(__FILE__) + '/../data/data_set'
require File.dirname(__FILE__) + '/../clusterers/single_linkage'

module Ai4r
  module Clusterers
     
    # Implementation of an Agglomerative Hierarchical clusterer with 
    # Ward's method linkage algorithm, aka the minimum variance method (Everitt
    # et al., 2001 ; Jain and Dubes, 1988 ; Ward, 1963 ).
    # Hierarchical clusteres create one cluster per element, and then 
    # progressively merge clusters, until the required number of clusters
    # is reached.
    # The objective of this method is to minime the variance. 
    # 
    #   D(cx, (ci U cj)) =  (ni/(ni+nj+nx))*D(cx, ci) + 
    #                       (nj/(ni+nj+nx))*D(cx, cj) - 
    #                       (nx/(ni+nj)^2)*D(ci, cj)
    class WardLinkage < SingleLinkage
      
    parameters_info :distance_function => 
          "Custom implementation of distance function. " +
          "It must be a closure receiving two data items and return the " +
          "distance bewteen them. By default, this algorithm uses " + 
          "ecuclidean distance of numeric attributes to the power of 2."
      
      # Build a new clusterer, using data examples found in data_set.
      # Items will be clustered in "number_of_clusters" different
      # clusters.
      def build(data_set, number_of_clusters)
        super
      end
      
      # This algorithms does not allow classification of new data items 
      # once it has been built. Rebuild the cluster including you data element.
      def eval(data_item)
        Raise "Eval of new data is not supported by this algorithm."
      end
      
      protected
       
      # return distance between cluster cx and cluster (ci U cj),
      # using ward's method linkage
      def linkage_distance(cx, ci, cj)
        ni = @index_clusters[ci].length
        nj = @index_clusters[cj].length
        nx = @index_clusters[cx].length
        ( ( ( 1.0* (ni+nx) * read_distance_matrix(cx, ci) ) +
            ( 1.0* (nj+nx) * read_distance_matrix(cx, cj) ) ) / (ni + nj + nx)  -
            ( 1.0 * nx * read_distance_matrix(ci, cj) / (ni+nj)**2 ) ) 
      end
      
    end
  end
end