Ruby script to calculate a simple linear regression line.(Ver.2)

regression_line_2.rb

#! /usr/local/bin/ruby
#*********************************************
# Ruby script to calculate a simple lenear regression line.
# : y = a + b * x
# : 連立方程式を ガウスの消去法で解く方法
#*********************************************
#
class Array
  def reg_line(y)
    # 以下の場合は例外スロー
    # - 引数の配列が Array クラスでない
    # - 自身配列が空
    # - 配列サイズが異なれば例外
    raise "Argument is not a Array class!"  unless y.class == Array
    raise "Self array is nil!"              if self.size == 0
    raise "Argument array size is invalid!" unless self.size == y.size

    sum_x  = self.inject(0) { |s, a| s += a }
    sum_y  = y.inject(0) { |s, a| s += a }
    sum_xx = self.inject(0) { |s, a| s += a * a }
    sum_xy = self.zip(y).inject(0) { |s, a| s += a[0] * a[1] }
    mtx = [
      [self.size,  sum_x,  sum_y],
      [    sum_x, sum_xx, sum_xy]
    ]
    ans = solve_ge(mtx)
    {intercept: ans[0][-1], slope: ans[1][-1]}
  end

  private

  # 連立方程式の解（ガウスの消去法）
  def solve_ge(a)
    n = a.size
    # 前進消去
    (n - 1).times do |k|
      (k + 1).upto(n - 1) do |i|
        d = a[i][k] / a[k][k].to_f
        (k + 1).upto(n) do |j|
          a[i][j] -= a[k][j] * d
        end
      end
    end
    # 後退代入
    (n - 1).downto(0) do |i|
      d = a[i][n]
      (i + 1).upto(n - 1) do |j|
        d -= a[i][j] * a[j][n]
      end
      a[i][n] = d / a[i][i].to_f
    end
    return a
  end
end

# 説明変数と目的変数
ary_x = [107, 336, 233, 82, 61, 378, 129, 313, 142, 428]
ary_y = [286, 851, 589, 389, 158, 1037, 463, 563, 372, 1020]
puts "説明変数 X = {#{ary_x.join(', ')}}"
puts "目的変数 Y = {#{ary_y.join(', ')}}"
puts "---"

# 単回帰直線算出（切片と傾き）
reg_line = ary_x.reg_line(ary_y)
puts "切片 a = #{reg_line[:intercept]}"
puts "傾き b = #{reg_line[:slope]}"