Betting simulation with GA

bet_ga.rb

require 'enumerator'

class BetGA
  attr_accessor :amount
  def initialize(o) # 初期化 入力：オッズ
    @odds = o
    @prob = Array.new() # 確率
    @bets = Array.new() # 投票
    @amounts = 50      # 購入数
    @populations = 50   # 生き残り
    @num_children = 500 # 子の数
    @mutation_p = 5.0     # 突然変異の確率（%）
    @adjust_p = 0.5
    calc_prob()
    first_generation()
  end
  
  def calc_sum(b) # 総投票数
    sum = 0.0
    b.each{|i|
      sum += i
    }
    sum
  end
  
  def calc_prob # 確率計算
    sum = 0.0
    @odds.each{|o|
      sum += 1/o.to_f
    }
    @odds.each_index{|i|
      @prob[i] = (1/@odds[i])/sum
    }
  end
  
  def first_generation # 初期シード
    @populations.times{|c|
      b = Array.new(@odds.length, 1)
      count = @amounts
      while count>0
        i = rand(@odds.length)
        a = (rand(10)-4).to_i
        b[i] += a
        if b[i]<1
          b[i] = 1
          count += a
        else
          count -= a
        end
      end
      @bets.push(b)
    }
  end
  
  def next_generation # 世代交代
    crossover()
    selection()
  end
  
  def crossover # 交叉
    @num_children.times{|i|
      p1 = @bets[rand(@bets.length)]
      p2 = @bets[rand(@bets.length)]
      child = co_two_point(p1,p2)
      @bets.push child
    }
  end
  
  def co_two_point(p1, p2) # 2点交叉
    c = Array.new
    cp1 = rand(p1.length)
    cp2 = rand(p1.length-cp1)
    c += p1.slice(0,cp1)
    c += p2.slice(cp1,cp2) if cp2>0
    c += p1.slice(cp1+cp2..p1.length)
    c = mutation(c)
    c = adapt(c)
    return c
  end
  
  def adapt(c)
    if rand(100)<@adjust_p
      c = adjust(c)
    end
    return c
  end
  
  def mutation(c) # 突然変異
    if rand(100)<@mutation_p
      p1 = rand(c.length)
      p2 = rand(c.length)
      t = c[p1]
      c[p1]=c[p2]
      c[p2]=t
    end
    return c
  end
  
  def selection # 選択
    result = sel_tournament(@bets, @populations, 1)
    @bets = result
  end
  
  def sel_tournament(children, div, limit) # div:分割数, limit:上位何位までか
    alived = Array.new
    children.each_slice(children.length/div){|s|
      alived.push(get_top_n(s, limit))
    }
    return alived
  end
  
  def get_top_n(c, n) # 上位n位まで抽出
    rank = Array.new
    result = Array.new
    c.each_index{|i|
      rank.push([i, evaluate(c[i])])
    }
    rank.sort!{|a,b|b[1]<=>a[1]}
    n.times{|i|
      result.push(c[rank[i][0]])
    }
    return result.flatten
  end
  
  def evaluate(bet) # 評価関数
#    return ev_v1(bet)
    return ev_v2(bet)
  end
  
  def average(a)
    s = 0.0
    a.each{|b| s += b}
    return s/a.length
  end
  
  def variance(a)
    s = 0.0
    av = average(a)
    a.each{|b|
      s += (b-av)*(b-av)
    }
    return s/(a.length-1)
  end
  
  def ev_v1(bet) # 評価関数
    sum = calc_sum(bet)
    e = 0.0
    rate=Array.new
    bet.each_index{|i|
      r = bet[i] * @odds[i]/sum
      if r>1.0 # 回収率100%以上のものについて加算
        if r<10 # 回収率1000%以上は無視
          e += bet[i] * @odds[i] * @prob[i]
        end
      else # 回収率100%未満は減点
        e -= (bet[i] * @odds[i] * @prob[i])
      end
    }
    return e
  end
  
  def ev_v2(bet)
    # rev1 分散が小さいほど高評価→回収率100%以下に収束しがち
    # rev2 回収率100%からの乖離率を加味する
    # rev3 期待値を加味する
    sum = calc_sum(bet)
    exp = 0.0
    rate=Array.new
    bet.each_index{|i|
      r = bet[i] * @odds[i]/sum
      rate.push(r)
      exp = @prob[i] * r
    }
    return exp * (120.0/(120.0-average(rate)).abs/100.0) * 1/(variance(rate))
  end
  
  def best
    get_top_n(@bets, 1)
  end
  
  def print_result(b) # 結果出力
    sum = calc_sum(b)
    puts "---------"
    @odds.length.times{|o|
      printf("%2d : %3d * %5.1f => %6d  %6.5f  %6.1f\n", o+1, b[o], @odds[o], b[o]*@odds[o], @prob[o], 100*b[o]*@odds[o]/sum)
    }
    puts "sum=#{sum}, E=#{evaluate(b)}"
  end
  
  def adjust(b) # 回収率100%以下に対して補正
    sum = calc_sum(b)
    b.each_index{|i|
      while (100*b[i]*@odds[i]/sum<100.0) and (calc_sum(b)<@amounts)
        b[i] += 1
      end
    }
    return b
  end
end
odds = Array.new()
#odds = [1.6, 2.6, 8.0, 13.3, 20.0]
# 20120520 tko 01R -> 15
odds[0] = [4.9,58.0,4.5,190.1,18.3,105.7,184.3,109.6,6.6,5.1,5.3,15.4,245.7,84.5,21.7,15.7]
# 20120520 tko 02R -> 16
odds[1] = [106.4,42.7,13.0,110.7,186.5,542.1,240.2,29.1,44.1,79.8,170.9,1.8,6.6,5.5,90.8,7.2]
# 20120520 tko 03R -> 14
odds[2] = [19.2,446.6,47.6,16.5,291.5,285.5,52.3,165.2,26.3,188.4,2.9,44.7,304.3,1.6,261.5,51.0]
# 20120520 tko 04R -> 05
odds[3] = [19.7,15.3,29.5,61.7,3.0,149.0,13.1,10.4,34.2,8.9,353.8,145.9,3.4,361.5,141.9,52.0,9.4]
# 20120520 tko 05R -> 07
odds[4] = [24.8,67.6,212.4,45.2,241.4,7.5,1.7,5.6,74.7,10.9,6.2]
# 20120520 tko 06R -> 11
odds[5] = [57.6,8.0,254.5,156.1,159.5,80.7,2.4,17.5,80.6,4.2,8.9,13.7,37.7,68.9,21.6,11.2]
# 20120520 tko 07R -> 02
odds[6] = [23.2,1.9,81.8,5.2,8.6,83.3,10.1,15.5,89.2,159.8,63.9,64.2,43.6,286.8,11.4,51.6]
# 20120520 tko 08R -> 07
odds[7] = [3.0,158.2,3.3,25.0,18.0,179.4,6.4,109.3,19.2,219.6,6.7,17.6,202.5,43.5,35.9,45.6]
# 20120520 tko 09R -> 16
odds[8] = [217.5,16.8,13.3,8.7,247.8,5.5,9.9,8.9,45.6,157.5,25.0,30.9,168.8,28.9,4.0,4.6]
# 20120520 tko 10R -> 11
odds[9] = [122.2,46.3,15.7,109.0,9.7,123.4,56.0,32.2,6.8,5.2,33.4,153.1,8.1,148.2,2.4,258.3,16.5]
# 20120520 tko 11R -> 14
odds[10] = [7.7, 78.6, 27.2, 39.0, 19.0, 79.4, 25.3, 3.3, 3.6, 15.5, 151.7, 58.5, 74.4, 5.6, 143.4, 23.0, 39.4, 83.3]
# 20120520 tko 12R -> 12
odds[11] = [4.4,68.1,25.3,6.0,144.4,31.8,135.9,5.1,5.6,3.6,47.0,11.0]

r=5
res=Array.new
b = BetGA.new(odds[r])
300.times{|i|
  puts "Generation: #{(i+1)}, E=#{b.evaluate(b.best)}"
  b.next_generation()
}
b.print_result(b.best)