Class: Classifier::ContentNode

Inherits:
Object
  • Object
show all
Defined in:
lib/classifier/lsi/content_node.rb

Overview

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.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(word_hash, *categories) ⇒ ContentNode

If text_proc is not specified, the source will be duck-typed via source.to_s



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# File 'lib/classifier/lsi/content_node.rb', line 18

def initialize( word_hash, *categories )
  @categories = categories || []
  @word_hash = word_hash
end

Instance Attribute Details

#categories ⇒ Object

Returns the value of attribute categories.



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# File 'lib/classifier/lsi/content_node.rb', line 11

def categories
  @categories
end

#lsi_norm ⇒ Object

Returns the value of attribute lsi_norm.



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# File 'lib/classifier/lsi/content_node.rb', line 11

def lsi_norm
  @lsi_norm
end

#lsi_vector ⇒ Object

Returns the value of attribute lsi_vector.



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# File 'lib/classifier/lsi/content_node.rb', line 11

def lsi_vector
  @lsi_vector
end

#raw_norm ⇒ Object

Returns the value of attribute raw_norm.



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# File 'lib/classifier/lsi/content_node.rb', line 11

def raw_norm
  @raw_norm
end

#raw_vector ⇒ Object

Returns the value of attribute raw_vector.



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# File 'lib/classifier/lsi/content_node.rb', line 11

def raw_vector
  @raw_vector
end

#word_hash ⇒ Object (readonly)

Returns the value of attribute word_hash.



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# File 'lib/classifier/lsi/content_node.rb', line 15

def word_hash
  @word_hash
end

Instance Method Details

#raw_vector_with(word_list) ⇒ Object

Creates the raw vector out of word_hash using word_list as the key for mapping the vector space.



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# 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.inject(0) { |sum,term| sum += term  }.to_f
  
  # 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 ⇒ Object

Use this to fetch the appropriate search vector in normalized form.



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# File 'lib/classifier/lsi/content_node.rb', line 29

def search_norm
  @lsi_norm || @raw_norm
end

#search_vector ⇒ Object

Use this to fetch the appropriate search vector.



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# File 'lib/classifier/lsi/content_node.rb', line 24

def search_vector
  @lsi_vector || @raw_vector
end