Adds Dual Transformer and Joss-Ann Transformer #746 #758
Conversation
| def dual_strategy(self, opponent): | ||
| from copy import deepcopy | ||
| fresh_opponent = deepcopy(opponent) | ||
| self.original.play(fresh_opponent) |
There was a problem hiding this comment.
should this be self.original_player.play?
That appears to be the name of the attribute which is set at line 187 in strategy_transformers.py
There was a problem hiding this comment.
I believe this method is not actually necessary (the transformer does not use it). Can it just be removed?
There was a problem hiding this comment.
(There's no need to modify the player class at all, it can all just be done as a transformer.)
| def noisy_wrapper(player, opponent, action, noise=0.05): | ||
| """Applies flip_action at the class level.""" | ||
| r = random.random() | ||
| if r < noise: | ||
| return flip_action(action) | ||
| return action | ||
|
|
||
|
|
| MixedTransformer = StrategyTransformerFactory( | ||
| mixed_wrapper, name_prefix="Mutated") | ||
|
|
||
|
|
||
| def joss_ann_wrapper(player, opponent, proposed_action, probability): | ||
| """Wraps the players strategy function to produce the Joss-Ann. |
There was a problem hiding this comment.
Why is it called the Joss-Ann? Is that a reference to some paper somewhere? If so, can we have a link in here?
There was a problem hiding this comment.
Can we include the bibliographic reference too as well (so with [Ashlock...]_ and the relevant info in the bibliography.rst file.
| return action | ||
|
|
||
|
|
||
| JossAnnTransformer = StrategyTransformerFactory(joss_ann_wrapper, |
There was a problem hiding this comment.
Can we have both parameters on one new line to match the other transformer definitions?
|
|
||
| The Dual of a strategy will return the exact opposite set of moves to the | ||
| original strategy when both are faced with the same history. | ||
|
|
There was a problem hiding this comment.
This needs a reference to Ashlock's paper so we can see where it comes from.
| MixedTransformer = StrategyTransformerFactory( | ||
| mixed_wrapper, name_prefix="Mutated") | ||
|
|
||
|
|
||
| def joss_ann_wrapper(player, opponent, proposed_action, probability): | ||
| """Wraps the players strategy function to produce the Joss-Ann. |
There was a problem hiding this comment.
Can we include the bibliographic reference too as well (so with [Ashlock...]_ and the relevant info in the bibliography.rst file.
| probability += (remaining_probability,) | ||
| options = [C, D, proposed_action] | ||
| action = choice(options, p=probability) | ||
| return action |
There was a problem hiding this comment.
Could we not implement this as a particular case of the Mixed Strategy transformer (by mixing between the probe and Cooperator and Defector)? This would reduce duplication.
| def dual_strategy(self, opponent): | ||
| from copy import deepcopy | ||
| fresh_opponent = deepcopy(opponent) | ||
| self.original.play(fresh_opponent) |
There was a problem hiding this comment.
I believe this method is not actually necessary (the transformer does not use it). Can it just be removed?
|
|
||
| The Dual of a strategy will return the exact opposite set of moves to the | ||
| original strategy when both are faced with the same history. | ||
|
|
So should I put a link to the relevant paper (in both the Dual and JossAnn Transformers) and then also add something to |
|
Yes please. |
|
(And in the docs you'll be able to point to the reference in the bibliography. Sphinx will render it nicely.) |
Adds Dual Transformer and Joss-Ann Transformer. These are both required when creating the fingerprint of a strategy as described in http://doi.org/10.1109/ITW.2010.5593352
These are no tests for these yet, but I thought I'd open a pull request to make sure people are happy with the implementation.
Edit - there are now tests.