class Classifier::ContentNode
This is an internal data structure class for the LSI node. Save for #raw_vector_with, it should be fairly straightforward to understand. You should never have to use it directly.
Attributes
categories[RW]
lsi_norm[RW]
lsi_vector[RW]
raw_norm[RW]
raw_vector[RW]
word_hash[R]
Public Class Methods
new( word_hash, *categories )
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If text_proc is not specified, the source will be duck-typed via source.to_s
# File lib/classifier/lsi/content_node.rb, line 18 def initialize( word_hash, *categories ) @categories = categories || [] @word_hash = word_hash end
Public Instance Methods
raw_vector_with( word_list )
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Creates the raw vector out of #word_hash using word_list as the key for mapping the vector space.
# File lib/classifier/lsi/content_node.rb, line 35 def raw_vector_with( word_list ) if $GSL vec = GSL::Vector.alloc(word_list.size) else vec = Array.new(word_list.size, 0) end @word_hash.each_key do |word| vec[word_list[word]] = @word_hash[word] if word_list[word] end # Perform the scaling transform total_words = vec.sum # Perform first-order association transform if this vector has more # than one word in it. if total_words > 1.0 weighted_total = 0.0 vec.each do |term| if ( term > 0 ) weighted_total += (( term / total_words ) * Math.log( term / total_words )) end end vec = vec.collect { |val| Math.log( val + 1 ) / -weighted_total } end if $GSL @raw_norm = vec.normalize @raw_vector = vec else @raw_norm = Vector[*vec].normalize @raw_vector = Vector[*vec] end end
search_norm()
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Use this to fetch the appropriate search vector in normalized form.
# File lib/classifier/lsi/content_node.rb, line 29 def search_norm @lsi_norm || @raw_norm end
search_vector()
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Use this to fetch the appropriate search vector.
# File lib/classifier/lsi/content_node.rb, line 24 def search_vector @lsi_vector || @raw_vector end