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axelrod/strategy_transformers.py

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@@ -200,7 +200,7 @@ def generic_strategy_wrapper(player, opponent, proposed_action, *args,
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def flip_wrapper(player, opponent, action):
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"""Applies Action.flip() at the class level."""
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"""Flips the player's original actions."""
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return action.flip()
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@@ -240,7 +240,7 @@ def dual_wrapper(player, opponent, proposed_action):
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def noisy_wrapper(player, opponent, action, noise=0.05):
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"""Applies Action.flip() at the class level."""
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"""Flips the player's actions with probability: `noise`."""
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r = random.random()
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if r < noise:
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return action.flip()

docs/tutorials/further_topics/noisy_tournaments.rst

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@@ -3,7 +3,7 @@ Noisy tournaments
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A common variation on iterated prisoner’s dilemma tournaments is to add
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stochasticity in the choice of actions, simply called noise. This noise is
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introduced by flipping plays between ‘C’ and ‘D’ with some probability that is
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introduced by flipping plays between C and D with some probability that is
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applied to all plays after they are delivered by the player [Bendor1993]_.
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The presence of this persistent background noise causes some strategies to

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