+import json
import logging
import math
import sqlalchemy
+import sqlalchemy.sql.functions as sfunc
from datetime import timedelta
from sqlalchemy.orm import mapper
from sqlalchemy.orm import scoped_session
def __repr__(self):
return "<Map(%s, %s, %s)>" % (self.map_id, self.name, self.version)
+ def to_dict(self):
+ return {'map_id':self.map_id, 'name':self.name}
+
class Game(object):
def __init__(self, game_id=None, start_dt=None, game_type_cd=None,
if game_type_cd is None:
game_type_cd = self.game_type_cd
+ # we do not have the actual duration of the game, so use the
+ # maximum alivetime of the players instead
+ duration = 0
+ for d in session.query(sfunc.max(PlayerGameStat.alivetime)).\
+ filter(PlayerGameStat.game_id==self.game_id).\
+ one():
+ duration = d.seconds
+
scores = {}
alivetimes = {}
for (p,s,a) in session.query(PlayerGameStat.player_id,
filter(PlayerGameStat.alivetime > timedelta(seconds=0)).\
filter(PlayerGameStat.player_id > 2).\
all():
- scores[p] = s
+ # scores are per second
+ scores[p] = s/float(a.seconds)
alivetimes[p] = a.seconds
player_ids = scores.keys()
elos[pid] = PlayerElo(pid, game_type_cd)
for pid in player_ids:
- elos[pid].k = KREDUCTION.eval(elos[pid].games, alivetimes[pid], 0)
+ elos[pid].k = KREDUCTION.eval(elos[pid].games, alivetimes[pid],
+ duration)
+ if elos[pid].k == 0:
+ del(elos[pid])
+ del(scores[pid])
+ del(alivetimes[pid])
elos = self.update_elos(elos, scores, ELOPARMS)
for e in elos:
session.add(elos[e])
- # TODO: duels are also logged as DM for elo purposes
+ if game_type_cd == 'duel':
+ self.process_elos(session, "dm")
def update_elos(self, elos, scores, ep):
eloadjust = {}
for pid in elos.keys():
eloadjust[pid] = 0
- pids = elos.keys()
-
if len(elos) < 2:
return elos
+
+ pids = elos.keys()
+
for i in xrange(0, len(pids)):
ei = elos[pids[i]]
for j in xrange(i+1, len(pids)):
scorefactor_real = si / float(si + sj)
# estimated score factor by elo
- elodiff = min(ep.maxlogdistance, max(-ep.maxlogdistance, (ei.elo - ej.elo) * ep.logdistancefactor))
+ elodiff = min(ep.maxlogdistance, max(-ep.maxlogdistance,
+ (float(ei.elo) - float(ej.elo)) * ep.logdistancefactor))
scorefactor_elo = 1 / (1 + math.exp(-elodiff))
# how much adjustment is good?
adjustment = scorefactor_real - scorefactor_elo
eloadjust[ei.player_id] += adjustment
- eloadjust[ei.player_id] -= adjustment
+ eloadjust[ej.player_id] -= adjustment
for pid in pids:
- elos[pid].elo = max(elos[pid].elo + eloadjust[pid] * elos[pid].k * ep.global_K / float(len(elos) - 1), ep.floor)
+ elos[pid].elo = max(float(elos[pid].elo) + eloadjust[pid] * elos[pid].k * ep.global_K / float(len(elos) - 1), ep.floor)
elos[pid].games += 1
return elos
class PlayerElo(object):
def __init__(self, player_id=None, game_type_cd=None):
+
self.player_id = player_id
self.game_type_cd = game_type_cd
- self.elo = 0
- self.k = 0.0
self.score = 0
self.games = 0
+ self.elo = ELOPARMS.initial
+
+ def __repr__(self):
+ return "<PlayerElo(pid=%s, gametype=%s, elo=%s)>" % \
+ (self.player_id, self.game_type_cd, self.elo)
+
+
+class PlayerRank(object):
+
+ def nick_html_colors(self):
+ if self.nick is None:
+ return "Anonymous Player"
+ else:
+ return html_colors(self.nick)
+
def __repr__(self):
- return "<PlayerElo(%s, %s, %s)>" % (self.player_id, self.game_type_cd,
- self.elo)
+ return "<PlayerRank(pid=%s, gametype=%s, rank=%s)>" % \
+ (self.player_id, self.game_type_cd, self.rank)
def initialize_db(engine=None):
servers_table = MetaData.tables['servers']
player_nicks_table = MetaData.tables['player_nicks']
player_elos_table = MetaData.tables['player_elos']
+ player_ranks_table = MetaData.tables['player_ranks']
# now map the tables and the objects together
mapper(PlayerAchievement, achievements_table)
mapper(Server, servers_table)
mapper(PlayerNick, player_nicks_table)
mapper(PlayerElo, player_elos_table)
+ mapper(PlayerRank, player_ranks_table)