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import csv
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import itertools
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- from numpy import mean , nanmedian , std , array , nan_to_num
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+ import numpy as np
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import tqdm
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import dask as da
@@ -284,7 +284,7 @@ def _build_payoff_matrix(self):
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for player_index , opponent_index in pairs :
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utilities = self .payoffs [player_index ][opponent_index ]
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if utilities :
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- payoff_matrix [player_index ][opponent_index ] = mean (utilities )
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+ payoff_matrix [player_index ][opponent_index ] = np . mean (utilities )
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return payoff_matrix
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@@ -298,14 +298,14 @@ def _build_payoff_stddevs(self):
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for player_index , opponent_index in pairs :
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utilities = self .payoffs [player_index ][opponent_index ]
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if utilities :
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- payoff_stddevs [player_index ][opponent_index ] = std (utilities )
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+ payoff_stddevs [player_index ][opponent_index ] = np . std (utilities )
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return payoff_stddevs
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@update_progress_bar
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def _build_payoff_diffs_means (self ):
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- payoff_diffs_means = [[mean (diff ) for diff in player ]
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+ payoff_diffs_means = [[np . mean (diff ) for diff in player ]
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for player in self .score_diffs ]
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return payoff_diffs_means
@@ -404,26 +404,26 @@ def _build_initial_cooperation_count(self, initial_cooperation_count_series):
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@update_progress_bar
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def _build_normalised_cooperation (self ):
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- normalised_cooperation = [list (nan_to_num (row ))
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- for row in array (self .cooperation ) /
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- sum (map (array , self .match_lengths ))]
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+ normalised_cooperation = [list (np . nan_to_num (row ))
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+ for row in np . array (self .cooperation ) /
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+ sum (map (np . array , self .match_lengths ))]
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return normalised_cooperation
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@update_progress_bar
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def _build_initial_cooperation_rate (self , interactions_series ):
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interactions_dict = interactions_series .to_dict ()
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- interactions_array = array ([interactions_series .get (player_index , 0 )
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- for player_index in range (self .num_players )])
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+ interactions_array = np . array ([interactions_series .get (player_index , 0 )
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+ for player_index in range (self .num_players )])
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initial_cooperation_rate = list (
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- nan_to_num (array (self .initial_cooperation_count ) /
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- interactions_array ))
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+ np . nan_to_num (np . array (self .initial_cooperation_count ) /
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+ interactions_array ))
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return initial_cooperation_rate
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@update_progress_bar
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def _build_ranking (self ):
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ranking = sorted (
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range (self .num_players ),
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- key = lambda i : - nanmedian (self .normalised_scores [i ]))
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+ key = lambda i : - np . nanmedian (self .normalised_scores [i ]))
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return ranking
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@update_progress_bar
@@ -637,8 +637,8 @@ def summarise(self):
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"""
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- median_scores = map (nanmedian , self .normalised_scores )
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- median_wins = map (nanmedian , self .wins )
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+ median_scores = map (np . nanmedian , self .normalised_scores )
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+ median_wins = map (np . nanmedian , self .wins )
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self .player = namedtuple ("Player" , ["Rank" , "Name" , "Median_score" ,
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"Cooperation_rating" , "Wins" ,
@@ -668,7 +668,7 @@ def summarise(self):
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if counter [(state , C )] > 0 ]
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if len (counts ) > 0 :
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- rate = mean (counts )
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+ rate = np . mean (counts )
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else :
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rate = 0
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