#!/usr/bin/env python


class Player:
  def __init__(self, team, name, rating):
    self.team    = team
    self.name    = name
    self.rating  = rating

class Match:
  def __init__(self, p1, p2, winner):
    self.p1 = p1
    self.p2 = p2
    self.winner = winner
  def loser(self):
    if self.winner == self.p1: return self.p2
    else: return self.p1
  def tostr(self):
    if self.p1 == self.winner:
      return "%s, %s, %0.4f" % (self.p1.name, self.p2.name, self.likelyhood())
    else:
      return "%s, %s, %0.4f" % (self.p2.name, self.p1.name, self.likelyhood())
  def likelyhood(self):
    return 1.0 / (1.0 + 10.0**((self.loser().rating-self.winner.rating)/400.0))

J1 = Player("J", "Murakaka Daisuke", 3216)
J2 = Player("J", "Iyama Yuta", 3550)
C1 = Player("C", "Lian Xiao", 3481)
C2 = Player("C", "Ke Jie", 3628)
K1 = Player("K", "Lee Sedol", 3523)

class NongshimIter(object):
  def __init__(self):
    self.teams = {}
    self.teams["J"] = [J1, J2]
    self.teams["C"] = [C1, C2]
    self.teams["K"] = [K1]
    self.matches = []
    self.next_team = None
    self.next_player = {}
    self.next_player["J"] = 0   # 0 based
    self.next_player["C"] = 0
    self.next_player["K"] = 0
    self.enumerate_all = []

  def state_tostr(self):
    s = ""
    for m in self.matches: s += m.tostr() + " "
    for k,v in self.next_player.iteritems():
      s += "np[%s]=%d " % (k, v)
    return s

  def __iter__(self):
    return self

  def set_next_match(self, p1, p2, winner, next_team):
    self.matches.append(Match(p1, p2, winner))
    self.matches.append(Match(p1, p2, winner))
    #print "set_next_match", self.matches[-1].tostr()
    self.next_team = next_team
    self.next_player[p2.team] += 1

  def undo_match(self):
    self.next_player[self.matches[-1].p2.team] -= 1
    del self.matches[-1]

  def gen(self, i):
    #print "next level", len(self.matches), i, self.state_tostr()
    #print
    if i > 5: return
    if len(self.matches) == 0:
      self.next_player["J"] += 1  # Init first player special case
      self.set_next_match(J1, K1, J1, "C")
      self.gen(i+1)
      self.undo_match()
      self.set_next_match(J1, K1, K1, "C")
      self.gen(i+1)
      self.undo_match()
    else:
      winner = self.matches[-1].winner
      loser = self.matches[-1].loser()
      #print self.next_team, self.next_team and len(self.teams[self.next_team])-1, self.next_team and self.next_player[self.next_team]
      if self.next_team and len(self.teams[self.next_team])-1 >= self.next_player[self.next_team]:
        p2 = self.teams[self.next_team][self.next_player[self.next_team]]
        self.set_next_match(winner, p2, winner, loser.team)
        self.gen(i+1)
        self.undo_match()
        self.set_next_match(winner, p2, p2, loser.team)
        self.gen(i+1)
        self.undo_match()
      elif len(self.teams[loser.team])-1 >= self.next_player[loser.team]:
        p2 = self.teams[loser.team][self.next_player[loser.team]]
        self.set_next_match(winner, p2, winner, None)
        self.gen(i+1)
        self.undo_match()
        self.set_next_match(winner, p2, p2, None)
        self.gen(i+1)
        self.undo_match()
      else:
        self.enumerate_all.append(self.matches[:])
        #print self.state_tostr()

n = NongshimIter()
n.gen(1)
for enum in n.enumerate_all:
  likelyhood = 1.0
  for m in enum:
    print m.tostr() + ",",
    likelyhood *= m.likelyhood()
  print "%s, %0.4f" % (enum[-1].winner.team, likelyhood)