Rubyでパーセプトロンを実装してみた　参照→http://syoshinsyakangeisagi.blogspot.com/2015/09/ruby.html

example01.rb

require "./perceptron"
require 'gnuplot'

learning_patterns = [
  [ [14], [17] ],
  [ [12], [5], [2], [-2] ],
  [ [-4], [-6], [-15] ]
]

perceptron = Perceptron.new(learning_patterns, 1)
perceptron.learn

output_file = "./example01.png"

Gnuplot.open do |gp|
  Gnuplot::Plot.new( gp ) do |plot|

    plot.title  "Example 01"
    plot.set "terminal png size 640, 300"
    plot.output output_file
    plot.xrange "[-20:20]"
    plot.yrange "[-1:1]"
    plot.xlabel "x"

    # 数直線
    lx = (-20..20).map { |v| v.to_f }
    ly = lx.map { |_| 0 }
    plot.data << Gnuplot::DataSet.new( [lx, ly] ) do |ds|
      ds.with = "lines"
      ds.linecolor = "rgb 'black'"
      ds.notitle
    end

    # 各学習パターン
    learning_patterns.each_with_index do |patterns, i|
      px = patterns
      py = px.map { |_| 0 }

      plot.data << Gnuplot::DataSet.new( [px, py] ) do |ds|
        ds.with = "points ls 1 pt 7 ps 2 lc #{i}"
        ds.notitle
      end
    end

    # 決定境界
    perceptron.weight_vectors.map{ |v| v.map(&:to_f) }.tap do |ary|
      break (ary.size - 1).times.map { |i| ary[i, 2] }
    end.each do |wi, wj|
      y = (-1..1).map(&:to_f)
      x = y.map { |_| (wi[0] - wj[0]) / (wj[1] - wi[1]) }

      plot.data << Gnuplot::DataSet.new( [x, y] ) do |ds|
        ds.with = "lines ls 1 lc 2 lw 2"
        ds.notitle
      end
    end
  end
end

example02.rb

require "./perceptron"
require 'gnuplot'

learning_patterns = [
  [ [-3, 3], [2, 8], [5, 3], [8, 5], [-7, 4], [-5, -2] ],
  [ [-8, -9], [-2, -8], [5, -9], [5, 0], [0, -5] ]
]

perceptron = Perceptron.new(learning_patterns, 2)
perceptron.learn

output_file = "./example02.png"

Gnuplot.open do |gp|
  Gnuplot::Plot.new( gp ) do |plot|

    plot.title  "Example 02"
    plot.set "terminal png size 480, 480"
    plot.output output_file
    plot.xrange "[-10:10]"
    plot.yrange "[-10:10]"
    plot.xlabel "x"
    plot.ylabel "y"

    # 各学習パターン
    learning_patterns.each_with_index do |patterns, i|
      px = patterns
      py = px.map { 0 }

      plot.data << Gnuplot::DataSet.new( [px, py] ) do |ds|
        ds.with = "points ls 1 pt 7 ps 2 lc #{i}"
        ds.notitle
      end
    end

    # 決定境界
    perceptron.weight_vectors.map{ |v| v.map(&:to_f) }.tap do |ary|
      break (ary.size - 1).times.map { |i| ary[i, 2] }
    end.each do |wi, wj|
      x = (-20..20).map(&:to_f)
      y = x.map do |v|
        v * ((wj[1]-wi[1]) / (wi[2]-wj[2])) + ((wj[0]-wi[0]) / (wi[2]-wj[2]))
      end

      plot.data << Gnuplot::DataSet.new( [x, y] ) do |ds|
        ds.with = "lines ls 1 lc 2 lw 2"
        ds.notitle
      end
    end
  end
end

perceptron.rb

require 'matrix'

class Perceptron
  RHO = 1
  attr_accessor :weight_vectors

  # 重みベクトルの初期化とか
  def initialize(learning_patterns, dimension)
    @learning_patterns = learning_patterns.map do |patterns|
      patterns.map { |pattern| Vector[1, *pattern] }
    end

    @class_num = @learning_patterns.size
    @weight_vectors = Array.new(@class_num) do |i|
      Vector[ *Array.new(dimension + 1) { i } ]
    end
  end

  # クラスiの識別関数 g_i(x)
  def discriminate(pattern, i)
    @weight_vectors[i].inner_product(pattern)
  end

  # 誤識別があった場合、重みベクトルを修正する
  def correct_errors
    @class_num.times do |i|
      @learning_patterns[i].each do |pattern|

        # jは識別結果
        j = @class_num.times.map do |c|
          { :class => c, :val => discriminate(pattern, c) }
        end.max_by { |e| e[:val] }[:class]

        # それが学習パターンのクラスと異なるなら修正
        if i != j
          @weight_vectors[i] += RHO * pattern
          @weight_vectors[j] -= RHO * pattern
        end
      end
    end
  end

  # 重みベクトルが更新されなくなるまで誤り訂正を繰り返す
  def learn
    correct_errors
    prev = nil
    while prev != @weight_vectors
      prev = Marshal.load(Marshal.dump(@weight_vectors))
      correct_errors
    end
  end
end