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Refactor tests for memory one #1032

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Jun 1, 2017
Merged

Refactor tests for memory one #1032

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f869970
Refactor tests for memory one strategies.
drvinceknight May 29, 2017
2642ccb
Add some references to strategies.
drvinceknight May 29, 2017
b170ca1
Use correct reference for memory 1 strategies.
drvinceknight May 31, 2017
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17 changes: 14 additions & 3 deletions axelrod/strategies/memoryone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,16 @@


class MemoryOnePlayer(Player):
"""Uses a four-vector for strategies based on the last round of play,
"""
Uses a four-vector for strategies based on the last round of play,
(P(C|CC), P(C|CD), P(C|DC), P(C|DD)), defaults to Win-Stay Lose-Shift.
Intended to be used as an abstract base class or to at least be supplied
with a initializing four_vector."""
with a initializing four_vector.

Names

- Memory One: [Nowak1990]_
"""

name = 'Generic Memory One Player'
classifier = {
Expand Down Expand Up @@ -191,7 +197,12 @@ def __init__(self) -> None:


class StochasticCooperator(MemoryOnePlayer):
"""Stochastic Cooperator, http://www.nature.com/ncomms/2013/130801/ncomms3193/full/ncomms3193.html."""
"""Stochastic Cooperator.

Names:

- Stochastic Cooperator: [Adami2013]_
"""

name = 'Stochastic Cooperator'

Expand Down
166 changes: 130 additions & 36 deletions axelrod/tests/strategies/test_memoryone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,11 +125,18 @@ def test_four_vector(self):

def test_strategy(self):
self.first_play_test(C)
self.responses_test([C], [C], [C])
self.responses_test([D], [C], [D])
self.responses_test([C], [D], [C])
self.responses_test([C], [D], [D], seed=1)
self.responses_test([D], [D], [D], seed=2)

actions = [(C, C), (C, D), (D, C), (C, D), (D, C)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions)

actions = [(C, D), (D, D), (D, D), (D, D), (C, D)]
self.versus_test(opponent=axelrod.Defector(),
expected_actions=actions, seed=0)

actions = [(C, D), (D, D), (C, D), (D, D), (D, D)]
self.versus_test(opponent=axelrod.Defector(),
expected_actions=actions, seed=1)


class TestStochasticCooperator(TestPlayer):
Expand All @@ -153,14 +160,22 @@ def test_four_vector(self):

def test_strategy(self):
self.first_play_test(C)
# With probability 0.065 will defect
self.responses_test([D, C, C, C], [C], [C], seed=15)
# With probability 0.266 will cooperate
self.responses_test([C], [C], [D], seed=1)
# With probability 0.42 will cooperate
self.responses_test([C], [D], [C], seed=3)
# With probability 0.229 will cooperate
self.responses_test([C], [D], [D], seed=13)

actions = [(C, C), (D, D), (C, C), (C, D), (C, C), (D, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=15)

actions = [(C, C), (C, D), (D, C), (D, D), (C, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=1)

actions = [(C, C), (C, D), (D, C), (D, D), (D, C), (D, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=3)

actions = [(C, C), (C, D), (D, C), (D, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=13)


class TestStochasticWSLS(TestPlayer):
Expand All @@ -179,14 +194,22 @@ class TestStochasticWSLS(TestPlayer):

def test_strategy(self):
self.first_play_test(C)
# With probability 0.05 will defect
self.responses_test([D], [C], [C], seed=2)
# With probability 0.05 will cooperate
self.responses_test([C], [C], [D], seed=31)
# With probability 0.05 will cooperate
self.responses_test([C], [D], [C], seed=31)
# With probability 0.05 will defect
self.responses_test([D], [D], [D], seed=2)

actions = [(C, C), (D, D), (C, C), (C, D), (D, C), (D, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, C), (C, D), (D, C), (D, D), (C, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=31)

actions = [(C, D), (D, C), (D, D), (C, C), (C, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=2)

actions = [(C, D), (C, C), (C, D), (D, C), (D, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=31)

def test_four_vector(self):
player = self.player()
Expand Down Expand Up @@ -240,10 +263,22 @@ def test_four_vector(self):

def test_strategy(self):
self.first_play_test(C)
self.responses_test([D, D, C, C], [C], [C], seed=2)
self.responses_test([D, D, C, C], [C], [D], seed=2)
self.responses_test([D, D, C, C], [D], [C], seed=2)
self.responses_test([D, D, C, C], [C], [D], seed=2)

actions = [(C, C), (D, D), (D, C), (D, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, C), (C, D), (C, C), (C, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=31)

actions = [(C, D), (D, C), (D, D), (D, C), (C, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=2)

actions = [(C, D), (C, C), (C, D), (C, C), (C, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=31)


class TestZDExtort2v2(TestPlayer):
Expand All @@ -268,6 +303,14 @@ def test_four_vector(self):
def test_strategy(self):
self.first_play_test(C)

actions = [(C, C), (D, D), (D, C), (D, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, D), (D, C), (D, D), (D, C), (D, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=5)


class TestZDExtort4(TestPlayer):

Expand All @@ -291,6 +334,14 @@ def test_four_vector(self):
def test_strategy(self):
self.first_play_test(C)

actions = [(C, C), (D, D), (D, C), (D, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, D), (D, C), (D, D), (D, C), (D, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=5)


class TestZDGen2(TestPlayer):

Expand All @@ -314,6 +365,22 @@ def test_four_vector(self):
def test_strategy(self):
self.first_play_test(C)

actions = [(C, C), (C, D), (D, C), (D, D), (C, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, C), (C, D), (C, C), (C, D), (C, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=31)

actions = [(C, D), (D, C), (D, D), (C, C), (C, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=2)

actions = [(C, D), (C, C), (C, D), (C, C), (C, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=31)


class TestZDGTFT2(TestPlayer):

Expand All @@ -335,10 +402,22 @@ def test_four_vector(self):

def test_strategy(self):
self.first_play_test(C)
self.responses_test([C, C, C, C], [C], [C], seed=2)
self.responses_test([D], [C], [D], seed=2)
self.responses_test([C, C, C, C], [D], [C], seed=2)
self.responses_test([D], [D], [D], seed=2)

actions = [(C, C), (C, D), (D, C), (C, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, C), (C, D), (C, C), (C, D), (C, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=31)

actions = [(C, D), (D, C), (C, D), (D, C), (C, D), (C, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=2)

actions = [(C, D), (C, C), (C, D), (C, C), (C, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=31)


class TestZDSet2(TestPlayer):
Expand All @@ -363,6 +442,14 @@ def test_four_vector(self):
def test_strategy(self):
self.first_play_test(C)

actions = [(C, C), (D, D), (D, C), (C, D), (C, C), (D, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, D), (D, C), (D, D), (D, C), (D, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=5)


class TestSoftJoss(TestPlayer):

Expand All @@ -383,8 +470,13 @@ def test_four_vector(self):
test_four_vector(self, expected_dictionary)

def test_strategy(self):
self.responses_test([C], [C], [C], seed=2)
self.responses_test([D], [C], [D], seed=5)
actions = [(C, C), (C, D), (D, C), (C, D), (D, C), (C, D)]
self.versus_test(opponent=axelrod.Alternator(),
expected_actions=actions, seed=2)

actions = [(C, D), (D, C), (C, D), (D, C), (C, D), (D, C)]
self.versus_test(opponent=axelrod.CyclerDC(),
expected_actions=actions, seed=5)


class TestALLCorALLD(TestPlayer):
Expand All @@ -402,8 +494,10 @@ class TestALLCorALLD(TestPlayer):
}

def test_strategy(self):
self.responses_test([D] * 10, seed=2)
self.responses_test([C] * 10, seed=3)
self.responses_test([C] * 10, seed=4)
self.responses_test([D] * 10, seed=5)
self.responses_test([D] * 10, seed=6)

actions = [(D, C)] * 10
self.versus_test(opponent=axelrod.Cooperator(),
expected_actions=actions, seed=0)
actions = [(C, C)] * 10
self.versus_test(opponent=axelrod.Cooperator(),
expected_actions=actions, seed=1)
4 changes: 3 additions & 1 deletion docs/reference/bibliography.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ Bibliography
This is a collection of various bibliographic items referenced in the
documentation.

.. [Adami2013] Adami C and Hintze A. (2013) Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything. Nature communications. https://www.nature.com/articles/ncomms3193
.. [Andre2013] Andre L. C., Honovan P., Felipe T. and Frederico G. (2013). Iterated Prisoner’s Dilemma - An extended analysis, http://abricom.org.br/wp-content/uploads/2016/03/bricsccicbic2013_submission_202.pdf
.. [Ashlock2006] Ashlock, D., & Kim E. Y, & Leahy, N. (2006). Understanding Representational Sensitivity in the Iterated Prisoner’s Dilemma with Fingerprints. IEEE Transactions On Systems, Man, And Cybernetics, Part C: Applications And Reviews, 36 (4)
.. [Ashlock2006b] Ashlock, W. & Ashlock, D. (2006). Changes in Prisoner's Dilemma Strategies Over Evolutionary Time With Different Population Sizes 2006 IEEE International Conference on Evolutionary Computation. http://DOI.org/10.1109/CEC.2006.1688322
Expand Down Expand Up @@ -33,7 +34,8 @@ documentation.
for the Iterated Prisoner's Dilemma. Proceedings of the 2015
International Conference on Autonomous Agents and Multiagent Systems.
.. [Nachbar1992] Nachbar J., Evolution in the finitely repeated prisoner’s dilemma, Journal of Economic Behavior & Organization, 19(3): 307-326, 1992.
.. [Nowak1992] Nowak, M. a., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature. http://doi.org/10.1038/359826a0
.. [Nowak1990] Nowak, M., & Sigmund, K. (1990). The evolution of stochastic strategies in the Prisoner's Dilemma. Acta Applicandae Mathematica. https://link.springer.com/article/10.1007/BF00049570
.. [Nowak1992] Nowak, M.., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature. http://doi.org/10.1038/359826a0
.. [Nowak1993] Nowak, M., & Sigmund, K. (1993). A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner’s Dilemma game. Nature, 364(6432), 56–58. http://doi.org/10.1038/364056a0
.. [Press2012] Press, W. H., & Dyson, F. J. (2012). Iterated Prisoner’s Dilemma contains strategies that dominate any evolutionary opponent. Proceedings of the National Academy of Sciences, 109(26), 10409–10413. http://doi.org/10.1073/pnas.1206569109
.. [Prison1998] LIFL (1998) PRISON. Available at: http://www.lifl.fr/IPD/ipd.frame.html (Accessed: 19 September 2016).
Expand Down