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        "author": "Pierre-Fran\u00e7ois MONVILLE",
        "author_email": "p_fmonville@hotmail.fr",
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            "Operating System :: OS Independent",
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        "description": "# whole_history_rating\na python convertion from the ruby implementation of R\u00c3\u00a9mi Coulom's Whole-History Rating (WHR) algorithm.\n\nthe original code can be found [here](https://github.com/goshrine/whole_history_rating)\n\n\nInstallation\n------------\n\n    pip install whole_history_rating\n\nUsage\n-----\n\n    from whr import whole_history_rating\n\n    whr = whole_history_rating.Base()\n\n    # Base.create_game() arguments: black player name, white player name, winner, day number, handicap\n    # Handicap should generally be less than 500 elo\n    whr.create_game(\"shusaku\", \"shusai\", \"B\", 1, 0)\n    whr.create_game(\"shusaku\", \"shusai\", \"W\", 2, 0)\n    whr.create_game(\"shusaku\", \"shusai\", \"W\", 3, 0)\n\n    # Iterate the WHR algorithm towards convergence with more players/games, more iterations are needed.\n    whr.iterate(50)\n\n    # Or let the module iterate until the elo is stable (precision by default 10E-3) with a time limit of 10 seconds by default\n    whr.auto_iterate(time_limit = 10, precision = 10E-3)\n\n    # Results are stored in one triplet for each day: [day_number, elo_rating, uncertainty]\n    whr.ratings_for_player(\"shusaku\") => \n      [[1, -43, 84], \n       [2, -45, 84], \n       [3, -45, 84]]\n    whr.ratings_for_player(\"shusai\") => \n      [[1, 43, 84], \n       [2, 45, 84], \n       [3, 45, 84]]\n\n    # You can print or get all ratings ordered\n    whr.print_ordered_ratings()\n    whr.get_ordered_ratings()\n\n    # You can get a prediction for a future game between two players (even non existing players)\n    # Base.probability_future_match() arguments: black player name, white player name, handicap\n    whr.probability_future_match(\"shusaku\", \"shusai\",0) =>\n      win probability: shusaku:37.24%; shusai:62.76%\n\n    # You can load several games all together using a file or a list of string representing the game\n    # all elements in list must be like: \"black_name white_name winner time_step handicap extras\" \n    # you can exclude handicap (default=0) and extras (default={})\n    whr.load_games([\"shusaku shusai B 1 0\", \"shusaku shusai W 2\", \"shusaku shusai W 3 0\"])\n    whr.load_games([\"firstname1 name1, firstname2 name2, W, 1\"], separator=\",\")\n\n    # You can save and load a base (you don't have to redo all iterations)\n    whr.save_base(path)\n    whr2 = whole_history_rating.Base.load_base(path)\n\n\nOptional Configuration\n----------------------\n\nOne of the meta parameters to WHR is the variance of rating change over one time step, :w2,\nwhich determines how much that a player's rating is likely change in one day.  Higher numbers allow for faster progress.\nThe default value is 300, which is fairly high.  \nR\u00c3\u00a9mi Coulom in his paper, used w2=14 to get his [results](https://www.remi-coulom.fr/WHR/WHR.pdf)\n\n\n    whr = whole_history_rating.Base({'w2':14})\n\n\n",
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