Gladstein strategy #1110
Gladstein strategy #1110
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axelrod/strategies/axelrod_second.py
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| This strategy is a TFT variant that defects on the first round in order to | ||
| test the opponent's response. If the opponent ever defects, the strategy | ||
| 'apologies' by cooperating and then plays TFT for the rest of the game. |
axelrod/strategies/axelrod_second.py
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| """ | ||
| Submitted to Axelrod's second tournament by David Gladstein. | ||
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| This strategy is also known as Tester and is based of the reverse |
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typo - should be 'is based on'
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| Names: | ||
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| - Gladstein: [Axelrod1980b]_ |
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Could you add the name Tester here, please?
axelrod/strategies/axelrod_second.py
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| super().__init__() | ||
| # This strategy assumes the opponent is a patsy | ||
| self.patsy = True | ||
| self.cooperation_ratio = 0 |
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There is no need to maintain this ratio between calls to the play method. The original fortran strategy had to do that, but we pass the entire history to the player at each turn, so it can always be calculated within the play method.
axelrod/strategies/axelrod_second.py
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| self.patsy = False | ||
| return C | ||
| # Cooperate as long as the cooperation ratio is below 0.5 | ||
| self.cooperation_ratio = self.cooperations / len(self.history) |
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As above, this can be calculated directly from the history without the need for an instance variable. For example:
from collections import Counter
cooperation_ratio = Counter(self.history)[C] / len(self.history)
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Actually, I was forgetting we have that count already built into the Player class, so it becomes even simpler:
cooperation_ratio = self.cooperations / len(self.history)
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Using self.cooperations is better, recomputing each time is O(n^2).
| actions = [(D, C), (C, C), (D, C), (C, D), (C, D)] | ||
| self.versus_test(opponent, expected_actions=actions, | ||
| attrs={'patsy': False, | ||
| 'cooperation_ratio': 0.3333333333333333}) |
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Could we have this as 'cooperation_ratio': 1/3.
axelrod/strategies/axelrod_second.py
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| name = "Gladstein" | ||
| classifier = { | ||
| 'memory_depth': 1, |
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This strategy has infinite memory depth : float('inf') as it keeps track of it's cooperations.
(You'll need to adjust the corresponding doctest.)
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I added the requested changes. I had to change a test in test_meta.py for |
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Could you also amend the overview of strategies doc to show that K76R has been implemented by this new class?
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Thanks for this @dmanc! |
This is the implementation of the K76R Fortran strategy mentioned in #1102. The strategy was named Gladstein after the author's last name.