Merge pull request #6 from endolith/patch-1

Fix formatting, typos in README.md

README.md

@@ -1,74 +1,80 @@
 # whole_history_rating
-a python convertion from the ruby implementation of Rémi Coulom's Whole-History Rating (WHR) algorithm.
+A python conversion from the ruby implementation of Rémi Coulom's Whole-History Rating (WHR) algorithm.
 
-the original code can be found [here](https://github.com/goshrine/whole_history_rating)
+The original ruby code can be found [here](https://github.com/goshrine/whole_history_rating)
 
 
 Installation
 ------------
 
-    pip install whole-history-rating
+```shell
+pip install whole-history-rating
+```
 
 Usage
 -----
 
-    from whr import whole_history_rating
-
-    whr = whole_history_rating.Base()
-
-    # Base.create_game() arguments: black player name, white player name, winner, day number, handicap
-    # Handicap should generally be less than 500 elo
-    whr.create_game("shusaku", "shusai", "B", 1, 0)
-    whr.create_game("shusaku", "shusai", "W", 2, 0)
-    whr.create_game("shusaku", "shusai", "W", 3, 0)
-
-    # Iterate the WHR algorithm towards convergence with more players/games, more iterations are needed.
-    whr.iterate(50)
-
-    # Or let the module iterate until the elo is stable (precision by default 10E-3) with a time limit of 10 seconds by default
-    whr.auto_iterate(time_limit = 10, precision = 10E-3)
-
-    # Results are stored in one triplet for each game: [day_number, elo_rating, uncertainty]
-    whr.ratings_for_player("shusaku") => 
-      [[1, -43, 84], 
-       [2, -45, 84], 
-       [3, -45, 84]]
-    whr.ratings_for_player("shusai") => 
-      [[1, 43, 84], 
-       [2, 45, 84], 
-       [3, 45, 84]]
-
-    # You can print or get all ratings ordered
-    whr.print_ordered_ratings(current=False) # current to True to only get the last rank estimation
-    whr.get_ordered_ratings(current=False, compact=False) # compact to True to not have the name before each ranks
-
-    # You can get a prediction for a future game between two players (even non existing players)
-    # Base.probability_future_match() arguments: black player name, white player name, handicap
-    whr.probability_future_match("shusaku", "shusai",0) =>
-      win probability: shusaku:37.24%; shusai:62.76%
-      
-    # You can load several games all together using a file or a list of string representing the game
-    # all elements in list must be like: "black_name white_name winner time_step handicap extras" 
-    # you can exclude handicap (default=0) and extras (default={})
-    whr.load_games(["shusaku shusai B 1 0", "shusaku shusai W 2", "shusaku shusai W 3 0"])
-    whr.load_games(["firstname1 name1, firstname2 name2, W, 1"], separator=",")
-
-    # You can save and load a base (you don't have to redo all iterations)
-    whr.save_base(path)
-    whr2 = whole_history_rating.Base.load_base(path)
-
-
+```py
+from whr import whole_history_rating
+
+whr = whole_history_rating.Base()
+
+# Base.create_game() arguments: black player name, white player name, winner, day number, handicap
+# Handicap should generally be less than 500 elo
+whr.create_game("shusaku", "shusai", "B", 1, 0)
+whr.create_game("shusaku", "shusai", "W", 2, 0)
+whr.create_game("shusaku", "shusai", "W", 3, 0)
+
+# Iterate the WHR algorithm towards convergence with more players/games, more iterations are needed.
+whr.iterate(50)
+
+# Or let the module iterate until the elo is stable (precision by default 10E-3) with a time limit of 10 seconds by default
+whr.auto_iterate(time_limit = 10, precision = 10E-3)
+
+# Results are stored in one triplet for each game: [day_number, elo_rating, uncertainty]
+whr.ratings_for_player("shusaku") => 
+  [[1, -43, 84], 
+   [2, -45, 84], 
+   [3, -45, 84]]
+whr.ratings_for_player("shusai") => 
+  [[1, 43, 84], 
+   [2, 45, 84], 
+   [3, 45, 84]]
+
+# You can print or get all ratings ordered
+whr.print_ordered_ratings(current=False) # current to True to only get the last rank estimation
+whr.get_ordered_ratings(current=False, compact=False) # compact to True to not have the name before each ranks
+
+# You can get a prediction for a future game between two players (even non existing players)
+# Base.probability_future_match() arguments: black player name, white player name, handicap
+whr.probability_future_match("shusaku", "shusai",0) =>
+  win probability: shusaku:37.24%; shusai:62.76%
+
+# You can load several games all together using a file or a list of string representing the game
+# all elements in list must be like: "black_name white_name winner time_step handicap extras" 
+# you can exclude handicap (default=0) and extras (default={})
+whr.load_games(["shusaku shusai B 1 0", "shusaku shusai W 2", "shusaku shusai W 3 0"])
+whr.load_games(["firstname1 name1, firstname2 name2, W, 1"], separator=",")
+
+# You can save and load a base (you don't have to redo all iterations)
+whr.save_base(path)
+whr2 = whole_history_rating.Base.load_base(path)
+```
+
 Optional Configuration
 ----------------------
 
-One of the meta parameters to WHR is the variance of rating change over one time step, :w2,
-which determines how much that a player's rating is likely change in one day.  Higher numbers allow for faster progress.
+One of the meta parameters to WHR is the variance of rating change over one time step, `w2`,
+which determines how much a player's rating is likely change in one day.  Higher numbers allow for faster progress.
 The default value is 300, which is fairly high.  
 Rémi Coulom in his paper, used w2=14 to get his [results](https://www.remi-coulom.fr/WHR/WHR.pdf)
 
-
-    whr = whole_history_rating.Base({'w2':14})
+```py
+whr = whole_history_rating.Base({'w2':14})
+```
 
 You can also set the base not case sensitive. "shusaku" and "ShUsAkU" will be the same.
 
-    whr = whole_history_rating.Base({'uncased':True})
+```py
+whr = whole_history_rating.Base({'uncased':True})
+```