Class: NekonekoGen::MLP

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

Overview

Multi Layer Perceptron

Constant Summary collapse

IR = 
0.4
HR = 
0.1
NOISE_VAR = 
0.3
MARGIN = 
0.2
DEFAULT_ITERATION = 
40

Instance Attribute Summary

Attributes inherited from Classifier

#k

Instance Method Summary collapse

Constructor Details

#initialize(k, options) ⇒ MLP

Returns a new instance of MLP.



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# File 'lib/nekoneko_gen/mlp.rb', line 16

def initialize(k, options)
  @k = k
  @output_units = @k == 2 ? 1 : @k
  @hidden_units = (options[:c] || default_hidden_unit).to_i
  @input = []
  @hidden = []
  @input_bias = []
  @hidden_bias = []
  @hidden_units.times do |i|
    @input[i] = Hash.new {|hash, key| hash[key] = default_value }
    @input_bias[i] = default_value
  end
  @output_units.times do |i|
    @hidden[i] = []
    @hidden_units.times do |j|
      @hidden[i][j] = default_value
    end
    @hidden_bias[i] = default_value
  end
end

Instance Method Details

#classify_method_code(lang) ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 146

def classify_method_code(lang)
  lang ||= :ruby
  case lang
  when :ruby
  else
    raise NotImplementedError
  end
  <<CODE
  def self.classify(vec)
input_y = []
HIDDEN_UNITS.times do |i|
  input_y[i] = sigmoid(INPUT_BIAS[i] +
                       INPUT_W[i].values_at(*vec).compact.reduce(0.0, :+))
end
if (K == 2)
  HIDDEN_BIAS[0] +
    input_y.zip(HIDDEN_W[0]).map{|a, b| a * b }.reduce(:+) > 0.0 ? 0 : 1
else
  K.times.map{|i|
    [HIDDEN_BIAS[i] + input_y.zip(HIDDEN_W[i]).map{|a, b| a * b }.reduce(:+), i]
  }.max.pop
end
  end
  def self.sigmoid(a)
1.0 / (1.0 + Math.exp(-a))
  end
CODE
end

#default_hidden_unit ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 13

def default_hidden_unit
  @k
end

#default_iteration ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 124

def default_iteration
  DEFAULT_ITERATION
end

#default_value ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 118

def default_value
  (rand - 0.5)
end

#features(i = -1)) ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 112

def features(i = -1)
  @input.map{|v| v.size }.reduce(:+)
end

#noise ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 121

def noise
  (Math.sqrt(-2.0 * Math.log(rand)) * Math.sin(2.0 * Math::PI * rand)) * NOISE_VAR
end

#parameter_code(lang, index_converter = lambda{|i| i}) ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 127

def parameter_code(lang, index_converter = lambda{|i| i})
  lang ||= :ruby
  case lang
  when :ruby
  else
    raise NotImplementedError
  end
  
  wvec = @input.map {|w|
    w.reduce({}) {|h, kv| h[index_converter.call(kv[0])] = kv[1]; h }
  }
  <<CODE
  HIDDEN_UNITS = #{@hidden_units}
  INPUT_BIAS = #{@input_bias.inspect}
  HIDDEN_BIAS = #{@hidden_bias.inspect}
  INPUT_W = JSON.load(#{wvec.to_json.inspect})
  HIDDEN_W = #{@hidden.inspect}
CODE
end

#sigmoid(a) ⇒ Object 



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# File 'lib/nekoneko_gen/mlp.rb', line 115

def sigmoid(a)
  1.0 / (1.0 + Math.exp(-a))
end

#update(vec, label) ⇒ Object 



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

def update(vec, label)
  input_y = []
  hidden_y = []
  output_y = []
  
  input_y = @hidden_units.times.map do |i|
    w = @input[i]
    sigmoid(@input_bias[i] + vec.map{|k, v| w[k] * v}.reduce(:+) + noise)
  end
  hidden_y = @output_units.times.map do |i|
    @hidden_bias[i] + input_y.zip(@hidden[i]).map{|a, b| a * b }.reduce(:+)
  end
  output_y = @output_units.times.map do |i|
    sigmoid(hidden_y[i])
  end
  
  loss = 0.0
  dotrain = false
  if (@output_units == 1)
    if (output_y[0] > 0.5)
      l = 0
    else
      l = 1
    end
    if (label == 0)
      if (output_y[0] < 1.0 - MARGIN)
        dotrain = true
      end
    else
      if (output_y[0] > MARGIN)
        dotrain = true
      end
    end
    loss = (label == l) ? 0.0 : 1.0
  else
    max_p, l = output_y.each_with_index.max
    if (l == label)
      if (max_p < 1.0 - MARGIN)
        dotrain = true
      end
    else
      loss = 1.0
      dotrain = true
    end
  end
  if (dotrain)
    output_bp = @output_units.times.map do |i|
      y = hidden_y[i]
      yt = (label == i) ? 1.0 : 0.0
      expy = Math.exp(y)
       -((2.0 * yt - 1.0) * expy + yt) / (Math.exp(2.0 * y) + 2.0 * expy + 1.0)
    end
    hidden_bp = @hidden_units.times.map do |j|
      y = 0.0
      @output_units.times do |i|
        y += output_bp[i] * @hidden[i][j]
      end
      y * (1.0 - input_y[j]) * input_y[j]
    end
    @output_units.times do |j|
      hidden = @hidden[j]
      @hidden_units.times do |i|
        hidden[i] -= HR * input_y[i] * output_bp[j]
      end
      @hidden_bias[j] -= HR * output_bp[j]
    end
    @hidden_units.times do |i|
      input = @input[i]
      vec.each do |k, v|
        input[k] -= IR * v * hidden_bp[i]
      end
      @input_bias[i] -= IR * hidden_bp[i]
    end
  end
  loss
end