Class: Eluka::FeatureVectors

Inherits:
Object
  • Object
show all
Defined in:
lib/eluka/feature_vector.rb

Instance Method Summary collapse

Constructor Details

#initialize(features, train) ⇒ FeatureVectors

Feature Vectors for a data point need to know the global list of features and their respective ids

During training, as we keep finding new features we add them to the features list

Hence we need to know whether the vectors we are computing are for training or classification



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# File 'lib/eluka/feature_vector.rb', line 25

def initialize (features, train)
  @fvs      = Array.new
  @features = features  #Instance of features
  @train    = train     #Boolean
end

Instance Method Details

#add(vector, label = 0) ⇒ Object

We just keep all data points stored and convert them to feature vectors only on demand



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# File 'lib/eluka/feature_vector.rb', line 34

def add (vector, label = 0)
  @fvs.push([vector, label])
end

#define_features ⇒ Object

For training data points we make sure all the features are added to the feature list



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# File 'lib/eluka/feature_vector.rb', line 41

def define_features
  @fvs.each do |vector, label|
    vector.each do |term, value|
      @features.add(term)
    end
  end
end

#to_libSVM(sel_features = nil) ⇒ Object

Creates feature vectors and converts them to LibSVM format – a multiline string with one data point per line

If provided with a list of selected features then insert only those features



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# File 'lib/eluka/feature_vector.rb', line 56

def to_libSVM (sel_features = nil)
  
  #Load the selected features into a Hash
  sf = Hash.new
  if (sel_features)
    sel_features.each do |f| 
      sf[f] = 1 
    end
  end
  
  self.define_features if (@train) #This method is needed only for training data
  
  output = Array.new
  @fvs.each do |vector, label|
    line = Array.new
    line.push(label)
    
    (1..@features.f_count).each do |id| #OPTIMIZE: Change this line to consider sorting in case of terms being features
      term = @features.term(id)
      if ( value = vector[term] ) then
        line.push([id, value].join(":")) if sf[term] or not sel_features
      end
    end
    output.push(line.join(" "))
  end
  output.join("\n")
end