Class: NekonekoGen::LinearClassifier

Inherits:
Classifier show all
Defined in:
lib/nekoneko_gen/linear_classifier.rb

Direct Known Subclasses

Arow, PA

Instance Attribute Summary collapse

Attributes inherited from Classifier

#k

Instance Method Summary collapse

Methods inherited from Classifier

#default_iteration

Instance Attribute Details

#bias ⇒ Object (readonly)

Returns the value of attribute bias.



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 7

def bias
  @bias
end

#w ⇒ Object (readonly)

Returns the value of attribute w.



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 7

def w
  @w
end

Instance Method Details

#classify_method_code(lang) ⇒ Object 



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 48

def classify_method_code(lang)
  lang ||= :ruby
  case lang
  when :ruby
  else
    raise NotImplementedError
  end
  <<CODE
  def self.classify(svec)
if (K == 2)
  w0 = W[0]
  (BIAS[0] + svec.map{|k, v| v * w0[k]}.reduce(0.0, :+)) > 0.0 ? 0 : 1
else
  W.each_with_index.map {|w, i|
    [BIAS[i] + svec.map{|k, v| v * w[k]}.reduce(0.0, :+), i]
  }.max.pop
end
  end
CODE
end

#dot(vec, w) ⇒ Object 



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 8

def dot(vec, w)
  dot = 0.0
  vec.each do |k, v|
    if (a = w[k])
      dot += a * v
    end
  end
  dot
end

#features(i = -1)) ⇒ Object 



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 29

def features(i = -1)
  if (i < 0)
    w.reduce(0){|sum, v| sum + v.size }
  else
    w[i].size
  end
end

#parameter_code(lang = :ruby) ⇒ Object 



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 36

def parameter_code(lang = :ruby)
  lang ||= :ruby
  case lang
  when :ruby
  else
    raise NotImplementedError
  end
  <<CODE
  BIAS = #{self.bias.inspect}
  W = JSON.load(#{@w.to_json.inspect})
CODE
end

#update(vec, label) ⇒ Object 



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# File 'lib/nekoneko_gen/linear_classifier.rb', line 17

def update(vec, label)
  loss = 0.0
  if (@k == 2)
    loss = update_at(0, vec, label)
  else
    s = 1.0 / @k
    @k.times do |i|
      loss += update_at(i, vec, label) * s
    end
  end
  loss
end