ランダムウォークによる広告アトリビューション予測モデル

random_walk.rb

# -*- coding: utf-8 -*-

require 'pp'


class RandomWalk
  def initialize(header, data, input)
    @header = header
    @data = data
    @input = input
    @loopmax = 10
    @cv_index = header.index("cv")
    @ridatsu_index = header.index("ridatsu")
    
  end
  def start
    @data = change_to_float(@data)
    @share_matrix = tr_share(@data)
    simulation()
  end
  def simulation
    calc_data = @input
    cv_data = []
    ridatasu_data = []
    
    0.upto(@loopmax) do |i|
      res = step(calc_data)
      cv_data << res[@cv_index]
      ridatasu_data << res[@ridatsu_index]
      calc_data = res[0...@cv_index]
      
      #CV数が1件以下になったら、ループ終了
      break if res[@cv_index] < 1
      #pp cv_data
      #pp ridatasu_data
      #pp calc_data
    end
    pp cv_data
    puts cv_data.sum()
    
  end
  def step(calc_data)
    res = []
    @share_matrix.each_with_index do |row,i|
      #pp row.inject(0){|a,b| a += b * 1000}
 
      arr = calc_data.map.with_index{|y, j|
        row[j] * y
      }
      res << arr.sum
    end

    return res
  end

####### Pre Processing ###########
#change to integer from file data
  def change_to_float(arr)
    res_arr = []
    arr.each do |x|
      res_arr << x.map{|y| y.to_f}
    end
    return res_arr
  end

#make share matrix transposed
  def tr_share(data)
  	arr = []
  	matrix = data[0...4]
    matrix.each do |x|
      if (x.sum != 0)
    		arr << x.map{|y| y / x.sum}
      end
    end
    return arr.transpose
  end


end


#Array拡張
class Array
  def sum
    s = 0.0
    n = 0
    self.each do |v|
      next if v.nil?
      s += v.to_f
      n += 1
    end
    return s
  end
 end





header = ["ad","sem","org","mail","cv","ridatsu"]

data = [
  [2,64,300,31,60,675],
  [2,48,74,61,10,219],
  [300,19,4,4,83,971],
  [32,51,94,3,0,265]
 ]

#入力データ
#input = [1000,1000,0,0]
input = [2000,0,0,0]

r = RandomWalk.new(header, data, input)
r.start()