forked from 0ad/0ad
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
"""Copyright (C) 2014 Wildfire Games.
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* This file is part of 0 A.D.
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*
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* 0 A.D. is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 2 of the License, or
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* (at your option) any later version.
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*
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* 0 A.D. is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with 0 A.D. If not, see <http://www.gnu.org/licenses/>.
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"""
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############ Constants ############
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# Difference between two ratings such that it is
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# regarded as a "sure win" for the higher player.
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# No points are gained or lost for such a game.
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elo_sure_win_difference = 600.0
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# Lower ratings "move faster" and change more
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# dramatically than higher ones. Anything rating above
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# this value moves at the same rate as this value.
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elo_k_factor_constant_rating = 2200.0
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# This preset number of games is the number of games
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# where a player is considered "stable".
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# Rating volatility is constant after this number.
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volatility_constant = 20.0
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# Fair rating adjustment loses against inflation
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# This constant will battle inflation.
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# NOTE: This can be adjusted as needed by a
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# bot/server administrator
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anti_inflation = 0.015
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############ Functions ############
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def get_rating_adjustment(rating, opponent_rating, games_played, opponent_games_played, result):
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"""
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Calculates the rating adjustment after a 1v1 game finishes using simplified ELO.
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Arguments:
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rating, opponent_rating - Ratings of the players before this game.
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games_played, opponent_games_played - Number of games each player has played
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before this game.
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result - 1 for the first player (rating, games_played) won, 0 for draw, or
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-1 for the second player (opponent_rating, opponent_games_played) won.
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Returns:
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The integer that should be subtracted from the loser's rating and added
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to the winner's rating to get their new ratings.
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TODO: Team games.
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"""
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player_volatility = (min(games_played, volatility_constant) / volatility_constant + 0.25) / 1.25
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rating_k_factor = 50.0 * (min(rating, elo_k_factor_constant_rating) / elo_k_factor_constant_rating + 1.0) / 2.0
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volatility = rating_k_factor * player_volatility
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difference = opponent_rating - rating
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if result == 1:
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return round(max(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation))
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elif result == -1:
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return round(min(0, (difference + result * elo_sure_win_difference) / volatility - anti_inflation))
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else:
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return round(difference / volatility - anti_inflation)
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# Inflation test - A slightly negative is better than a slightly positive
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# Lower rated players stop playing more often than higher rated players
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# Uncomment to test.
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# In this example, two evenly matched players play for 150000 games.
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"""
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from random import randrange
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r1start = 1600
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r2start = 1600
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r1 = r1start
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r2 = r2start
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for x in range(0, 150000):
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res = randrange(3)-1 # How often one wins against the other
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if res >= 1:
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res = 1
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elif res <= -1:
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res = -1
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r1gain = get_rating_adjustment(r1, r2, 20, 20, res)
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r2gain = get_rating_adjustment(r2, r1, 20, 20, -1 * res)
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r1 += r1gain
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r2 += r2gain
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print(str(r1) + " " + str(r2) + " : " + str(r1 + r2-r1start - r2start))
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"""
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