Class: NekonekoGen::PA

Inherits:
LinearClassifier show all
Defined in:
lib/nekoneko_gen/pa.rb

Overview

Passive Agressive

Constant Summary collapse

C = 
1.0
NORM =

norm + BIAS

2.0
DEFAULT_ITERATION = 
20

Instance Attribute Summary

Attributes inherited from LinearClassifier

#bias, #w

Attributes inherited from Classifier

#k

Instance Method Summary collapse

Methods inherited from LinearClassifier

#classify_method_code, #dot, #features, #parameter_code, #update

Methods inherited from Classifier

#classify_method_code, #features, #parameter_code, #update

Constructor Details

#initialize(k, n, options = {}) ⇒ PA

Returns a new instance of PA.



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# File 'lib/nekoneko_gen/pa.rb', line 11

def initialize(k, n, options = {})
  @k = k
  @c = options[:c] || C
  @w = []
  @bias = []
  if (@k == 2)
    @w[0] = Array.new(n, 0.0)
    @bias[0] = 0.0
  else
    k.times do |i|
      @w[i] = Array.new(n, 0.0)
      @bias[i] = 0.0
    end
  end
  if options[:method]
    @tau = 
      case options[:method]
      when :pa
        lambda{|y, l| pa(y, l)}
      when :pa1
        lambda{|y, l| pa1(y, l)}          
      when :pa2
        lambda{|y, l| pa2(y, l)}          
      else
        lambda{|y, l| pa2(y, l)}          
      end
  else
    @tau = lambda{|y, l| pa2(y, l)}
  end
end

Instance Method Details

#default_iteration ⇒ Object 



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# File 'lib/nekoneko_gen/pa.rb', line 65

def default_iteration
  DEFAULT_ITERATION
end

#pa(y, l) ⇒ Object 



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# File 'lib/nekoneko_gen/pa.rb', line 47

def pa(y, l)
  y * l / NORM
end

#pa1(y, l) ⇒ Object 



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# File 'lib/nekoneko_gen/pa.rb', line 44

def pa1(y, l)
  y * [@c, (l / NORM)].min
end

#pa2(y, l) ⇒ Object 



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# File 'lib/nekoneko_gen/pa.rb', line 41

def pa2(y, l)
  y * (l / (NORM + 0.5 / @c))
end

#update_at(i, vec, label) ⇒ Object 



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# File 'lib/nekoneko_gen/pa.rb', line 50

def update_at(i, vec, label)
  y = label == i ? 1 : -1
  w = @w[i]
  
  score = @bias[i] + dot(vec, w)
  l = 1.0 - score * y
  if (l > 0.0)
    alpha = @tau.call(y, l)
    vec.each do |k, v|
      w[k] += alpha * v
    end
    @bias[i] += alpha
  end
  y * score < 0.0 ? 1.0 : 0.0
end