Class: ClassifierReborn::ContentNode

Inherits:
Object
  • Object
show all
Defined in:
lib/classifier-reborn/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.

Direct Known Subclasses

CachedContentNode

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-reborn/lsi/content_node.rb', line 18

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

Instance Attribute Details

#categories ⇒ Object

Returns the value of attribute categories.



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# File 'lib/classifier-reborn/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-reborn/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-reborn/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-reborn/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-reborn/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-reborn/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-reborn/lsi/content_node.rb', line 41

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 and force floating point arithmetic
  if $GSL
    sum = 0.0
    vec.each {|v| sum += v }
    total_words = sum
  else
    total_words = vec.reduce(0, :+).to_f
  end

  total_unique_words = 0

  if $GSL
    vec.each { |word| total_unique_words += 1 if word != 0.0 }
  else
    total_unique_words = vec.count{ |word| word != 0 }
  end

  # Perform first-order association transform if this vector has more
  # then one word in it.
  if total_words > 1.0 && total_unique_words > 1
    weighted_total = 0.0
    # Cache calculations, this takes too long on large indexes
    cached_calcs = Hash.new { |hash, term|
      hash[term] = (( term / total_words ) * Math.log( term / total_words ))
    }

    vec.each do |term|
      weighted_total += cached_calcs[term] if term > 0.0
    end

    # Cache calculations, this takes too long on large indexes
    cached_calcs = Hash.new do |hash, val|
      hash[val] = Math.log( val + 1 ) / -weighted_total
    end

    vec.collect! { |val|
      cached_calcs[val]
    }
  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-reborn/lsi/content_node.rb', line 35

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-reborn/lsi/content_node.rb', line 25

def search_vector
  @lsi_vector || @raw_vector
end

#transposed_search_vector ⇒ Object

Method to access the transposed search vector



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# File 'lib/classifier-reborn/lsi/content_node.rb', line 30

def transposed_search_vector
  search_vector.col
end