Add new strategy SelfSteem

axelrod/strategies/_strategies.py

@@ -67,6 +67,7 @@
     Retaliate, Retaliate2, Retaliate3, LimitedRetaliate, LimitedRetaliate2,
     LimitedRetaliate3)
 from .sequence_player import SequencePlayer, ThueMorse, ThueMorseInverse
+from .selfsteem import SelfSteem
 from .shortmem import ShortMem
 from .titfortat import (
     TitForTat, TitFor2Tats, TwoTitsForTat, Bully, SneakyTitForTat,
@@ -210,6 +211,7 @@
     RevisedDowning,
     Ripoff,
     RiskyQLearner,
+    SelfSteem,
     ShortMem,
     Shubik,
     SlowTitForTwoTats,

axelrod/strategies/selfsteem.py

@@ -0,0 +1,53 @@
+from axelrod.actions import Action, Actions
+from axelrod.player import Player
+from axelrod.random_ import random_choice
+
+from math import pi, sin
+
+C, D = Actions.C, Actions.D
+
+
+class SelfSteem(Player):
+    """
+    This strategy is based on the feeling with the same name.
+    It is modeled on the sine curve(f = sin( 2* pi * n / 10 )), which varies
+    with the current iteration.
+
+    If f > 0.95, 'ego' of the algorithm is inflated; always defects.
+    If 0.95 > abs(f) > 0.3, rational behavior; follows TitForTat algortithm.
+    If 0.3 > f > -0.3; random behavior.
+    If f < -0.95, algorithm is at rock bottom; always cooperates.
+
+    Futhermore, the algorithm implements a retaliation policy, if the opponent
+    defects; the sin curve is shifted. But due to lack of further information,
+    this implementation does not include a sin phase change.
+    Names:
+
+    - SelfSteem: [Andre2013]_
+    """
+
+    name = 'SelfSteem'
+    classifier = {
+        'memory_depth': float("inf"),
+        'stochastic': True,
+        'makes_use_of': set(),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent: Player) -> Action:
+        turns_number = len(self.history)
+        sine_value =  sin(2 * pi * turns_number / 10)
+
+        if sine_value > 0.95:
+            return D
+
+        if abs(sine_value) < 0.95 and abs(sine_value) > 0.3:
+            return opponent.history[-1]
+
+        if sine_value < 0.3 and sine_value > -0.3:
+            return random_choice()
+
+        return C

axelrod/tests/unit/test_selfsteem.py

@@ -0,0 +1,38 @@
+"""Tests for the SelfSteem strategy."""
+
+import axelrod
+import random
+from .test_player import TestPlayer
+
+C, D = axelrod.Actions.C, axelrod.Actions.D
+
+class TestSelfSteem(TestPlayer):
+
+    name = "SelfSteem"
+    player = axelrod.SelfSteem
+    expected_classifier = {
+        'memory_depth': float("inf"),
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        # Check for f > 0.95
+        self.responses_test([D], [C] * 2 , [C] * 2)
+        self.responses_test([D], [C] * 13, [C] * 13)
+
+        # Check for f < -0.95
+        self.responses_test([C], [C] * 7, [C] * 7)
+        self.responses_test([C], [C] * 18, [D] * 18)
+
+        # Check for -0.3 < f < 0.3
+        self.responses_test([C], [C] * 20, [C] * 20, seed=6)
+        self.responses_test([D], [C] * 20, [D] * 20, seed=5)
+
+        # Check for 0.95 > abs(f) > 0.3
+        self.responses_test([C], [C], [C])
+        self.responses_test([D], [C] * 16, [D] * 16)
+        self.responses_test([C], [D] * 9, [C] * 9)

docs/reference/all_strategies.rst

@@ -135,6 +135,9 @@ Here are the docstrings of all the strategies in the library.
 .. automodule:: axelrod.strategies.shortmem
    :members:
    :undoc-members:
+.. automodule:: axelrod.strategies.selfsteem
+   :members:
+   :undoc-members:   
 .. automodule:: axelrod.strategies.titfortat
    :members:
    :undoc-members:

docs/reference/bibliography.rst

@@ -6,6 +6,7 @@ Bibliography
 This is a collection of various bibliographic items referenced in the
 documentation.
 
+.. [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)
 .. [Ashlock2008] Ashlock, D., & Kim, E. Y. (2008). Fingerprinting: Visualization and automatic analysis of prisoner’s dilemma strategies. IEEE Transactions on Evolutionary Computation, 12(5), 647–659. http://doi.org/10.1109/TEVC.2008.920675
 .. [Ashlock2009] Ashlock, D., Kim, E. Y., & Ashlock, W. (2009) Fingerprint analysis of the noisy prisoner’s dilemma using a finite-state representation. IEEE Transactions on Computational Intelligence and AI in Games. 1(2), 154-167  http://doi.org/10.1109/TCIAIG.2009.2018704
@@ -37,4 +38,3 @@ documentation.
 .. [Stewart2012] Stewart, a. J., & Plotkin, J. B. (2012). Extortion and cooperation in the Prisoner’s Dilemma. Proceedings of the National Academy of Sciences, 109(26), 10134–10135. http://doi.org/10.1073/pnas.1208087109
 .. [Szabó1992] Szabó, G., & Fáth, G. (2007). Evolutionary games on graphs. Physics Reports, 446(4-6), 97–216. http://doi.org/10.1016/j.physrep.2007.04.004
 .. [Tzafestas2000] Tzafestas, E. (2000). Toward adaptive cooperative behavior. From Animals to Animals: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior {(SAB-2000)}, 2, 334–340.
-.. [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

docs/tutorials/advanced/classification_of_strategies.rst

@@ -47,7 +47,7 @@ strategies::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    64
+    65
 
 Or, to find out how many strategies only use 1 turn worth of memory to
 make a decision::