Class: Brain::NeuralNetwork

Inherits:
Object
  • Object
show all
Defined in:
lib/brain/neuralnetwork.rb

Instance Method Summary collapse

Constructor Details

#initialize(options = {}) ⇒ NeuralNetwork 



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# File 'lib/brain/neuralnetwork.rb', line 5

def initialize(options = {})
  @learning_rate = options[:learning_rate] || 0.3
  @momentum = options[:momentum] || 0.1
  @hidden_sizes = options[:hidden_layers]
  @binary_thresh = options[:binary_thresh] || 0.5
end

Instance Method Details

#adjust_weights(learning_rate) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 145

def adjust_weights(learning_rate)
  (1..@output_layer).each do |layer|
    incoming = @outputs[layer - 1]

    (0...@sizes[layer]).each do |node|
      delta = @deltas[layer][node]

      (0...incoming.length).each do |k|
        change = @changes[layer][node][k]

        change = (learning_rate * delta * incoming[k]) + (@momentum * change)

        @changes[layer][node][k] = change
        @weights[layer][node][k] += change
      end
      @biases[layer][node] += learning_rate * delta
    end
  end
end

#calculate_deltas(target) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 125

def calculate_deltas(target)
  (0..@output_layer).to_a.reverse.each do |layer|
    (0...@sizes[layer]).each do |node|
      output = @outputs[layer][node]

      error = 0
      if layer == @output_layer
        error = target[node] - output
      else
        deltas = @deltas[layer + 1]
        (0...deltas.length).each do |k|
          error += deltas[k] * @weights[layer + 1][k][node]
        end
      end
      @errors[layer][node] = error
      @deltas[layer][node] = error * output * (1 - output)
    end
  end
end

#format_data(data) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 165

def format_data(data)
  unless data.is_a? Array
    data = [data]
  end

  #turn sparse hash input into arrays with 0s as filler
  unless data[0][:input].is_a? Array
    @input_lookup = Lookup.build_lookup data.map {|d| d[:input]} unless @input_lookup
    data.map! do |datum|
      array = Lookup.to_array @input_lookup, datum[:input]
      datum.merge({ input: array })
    end
  end

  unless data[0][:output].is_a? Array
    @output_lookup = Lookup.build_lookup data.map {|d| d[:output]} unless @output_lookup
    data.map! do |datum|
      array = Lookup.to_array @output_lookup, datum[:output]
      datum.merge({ output: array })
    end
  end

  data
end

#init(sizes) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 12

def init(sizes)
  @sizes = sizes
  @output_layer = @sizes.length - 1

  @biases = [] # weights for bias nodes
  @weights = []
  @outputs = []

  # state for training
  @deltas = []
  @changes = [] # for momentum
  @errors = []

  (0..@output_layer).each do |layer|
    size = @sizes[layer]
    @deltas[layer] = Array.new size, 0
    @errors[layer] = Array.new size, 0
    @outputs[layer] = Array.new size, 0

    if layer > 0
      @biases[layer] = randos size
      @weights[layer] = Array.new size
      @changes[layer] = Array.new size

      (0...size).each do |node|
        prev_size = @sizes[layer - 1]
        @weights[layer][node] = randos prev_size
        @changes[layer][node] = Array.new prev_size, 0
      end
    end
  end
end

#run(input) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 45

def run(input)
  input = Lookup.to_array(@input_lookup, input) if @input_lookup

  output = run_input input
  output = Lookup.to_hash(@output_lookup, output) if @output_lookup

  output
end

#run_input(input) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 54

def run_input(input)
  @outputs[0] = input
  output = 0

  (1..@output_layer).each do |layer|
    (0...@sizes[layer]).each do |node|
      weights = @weights[layer][node]

      sum = @biases[layer][node]
      (0...weights.length).each do |k|
        sum += weights[k] * input[k]
      end
      @outputs[layer][node] = 1 / (1 + Math.exp(-sum))
    end
    output = input = @outputs[layer]
  end

  output
end

#train(data, options = {}) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 74

def train(data, options = {})
  data = format_data data

  iterations = options[:iterations] || 20000
  error_thresh = options[:error_thresh] || 0.003
  log = options[:log] || false
  log_period = options[:log_period] || 10
  learning_rate = options[:learning_rate] || @learning_rate || 0.3

  input_size = data[0][:input].length
  output_size = data[0][:output].length

  hidden_sizes = @hidden_sizes
  hidden_sizes = [[3, (input_size / 2.0).floor].max] unless hidden_sizes
  sizes = [input_size, hidden_sizes, output_size].flatten
  init sizes

  error = 1
  done_iterations = iterations
  (0...iterations).each do |i|
    unless error > error_thresh
      done_iterations = i
      break
    end
    sum = 0
    data.each do |d|
      err = train_pattern d[:input], d[:output], learning_rate
      sum += err
    end
    error = sum / data.length

    puts "iterations: #{i}, training error: #{error}" if log and (i % log_period == 0)
  end

  {
    error: error,
    iterations: done_iterations
  }
end

#train_pattern(input, target, learning_rate) ⇒ Object 



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# File 'lib/brain/neuralnetwork.rb', line 114

def train_pattern(input, target, learning_rate)
  learning_rate ||= @learning_rate

  # forward propogate
  run_input input
  calculate_deltas target
  adjust_weights learning_rate

  mse @errors[@output_layer]
end