Add Gradual Killer strategy

Author: geraintpalmer

axelrod/strategies/_strategies.py

@@ -27,6 +27,7 @@
     GoByMajority5,
     HardGoByMajority, HardGoByMajority10, HardGoByMajority20, HardGoByMajority40,
     HardGoByMajority5)
+from .gradualkiller import GradualKiller
 from .grudger import (Grudger, ForgetfulGrudger, OppositeGrudger, Aggravater,
     SoftGrudger)
 from .grumpy import Grumpy
@@ -120,6 +121,7 @@
     GoByMajority5,
     Golden,
     Gradual,
+    GradualKiller,
     Grofman,
     Grudger,
     Grumpy,

axelrod/strategies/gradualkiller.py

@@ -0,0 +1,37 @@
+from axelrod import Actions, Player, init_args
+C, D = Actions.C, Actions.D
+
+
+class GradualKiller(Player):
+    """
+    It begins by defecting in the first five moves, then cooperates two times.
+    It then defects all the time if the opponent has defected in move 6 and 7,
+    else cooperates all the time.
+
+    Names
+
+    - Gradual Killer: [PRISON1998]_
+    """
+
+    # These are various properties for the strategy
+    name = 'Gradual Killer'
+    classifier = {
+        'memory_depth': float('Inf'),
+        'stochastic': False,
+        'makes_use_of': set(),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent):
+        """This is the actual strategy"""
+        # First seven moves
+        firstseven = [D, D, D, D, D, C, C]
+        if len(self.history) < 7:
+            return firstseven[len(self.history)]
+        # React to the opponent's 6th and 7th moves
+        elif opponent.history[5:7] == [D, D]:
+            return D
+        return C

axelrod/tests/unit/test_gradualkiller.py

@@ -0,0 +1,104 @@
+"""Test for the gradual killer strategy."""
+
+import axelrod
+from .test_player import TestHeadsUp, TestPlayer
+
+from hypothesis import given
+from hypothesis.strategies import integers
+from axelrod.tests.property import strategy_lists
+
+import random
+
+C, D = axelrod.Actions.C, axelrod.Actions.D
+
+
+class TestGradualKiller(TestPlayer):
+
+    name = "Gradual Killer"
+    player = axelrod.GradualKiller
+    expected_classifier = {
+        'memory_depth': float('Inf'),
+        'stochastic': False,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        """Starts by Defecting."""
+        self.first_play_test(D)
+
+    def test_effect_of_strategy(self):
+        """Fist seven moves."""
+        self.markov_test([D, D, D, D])
+        self.responses_test([], [], [D, D, D, D, D, C, C])
+
+    def test_effect_of_strategy_with_history_CC(self):
+        """Continues with C if opponent played CC on 6 and 7."""
+        P1 = axelrod.GradualKiller()
+        P2 = axelrod.Player()
+        P1.history = [D, D, D, D, D, C, C]
+        P2.history = [C, C, C, C, C, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C]
+        P2.history = [C, C, C, C, C, C, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C]
+        P2.history = [C, C, C, C, C, C, C, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C, C]
+        P2.history = [C, C, C, C, C, C, C, C, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+
+    def test_effect_of_strategy_with_history_CD(self):
+        """Continues with C if opponent played CD on 6 and 7."""
+        P1 = axelrod.GradualKiller()
+        P2 = axelrod.Player()
+        P1.history = [D, D, D, D, D, C, C]
+        P2.history = [C, C, C, C, C, C, D]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C]
+        P2.history = [C, C, C, C, C, C, D, D]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C]
+        P2.history = [C, C, C, C, C, C, D, D, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C, C]
+        P2.history = [C, C, C, C, C, C, D, D, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+
+    def test_effect_of_strategy_with_history_DC(self):
+        """Continues with C if opponent played DC on 6 and 7."""
+        P1 = axelrod.GradualKiller()
+        P2 = axelrod.Player()
+        P1.history = [D, D, D, D, D, C, C]
+        P2.history = [C, C, C, C, C, D, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C]
+        P2.history = [C, C, C, C, C, D, C, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C]
+        P2.history = [C, C, C, C, C, D, C, C, D]
+        self.assertEqual(P1.strategy(P2), 'C')
+        P1.history = [D, D, D, D, D, C, C, C, C, C]
+        P2.history = [C, C, C, C, C, D, C, C, D, C]
+        self.assertEqual(P1.strategy(P2), 'C')
+
+    def test_effect_of_strategy_with_history_CC(self):
+        """Continues with D if opponent played DD on 6 and 7."""
+        P1 = axelrod.GradualKiller()
+        P2 = axelrod.Player()
+        P1.history = [D, D, D, D, D, C, C]
+        P2.history = [C, C, C, C, C, D, D]
+        self.assertEqual(P1.strategy(P2), 'D')
+        P1.history = [D, D, D, D, D, C, C, D]
+        P2.history = [C, C, C, C, C, D, D, C]
+        self.assertEqual(P1.strategy(P2), 'D')
+        P1.history = [D, D, D, D, D, C, C, D, D]
+        P2.history = [C, C, C, C, C, D, D, C, C]
+        self.assertEqual(P1.strategy(P2), 'D')
+        P1.history = [D, D, D, D, D, C, C, D, D, D]
+        P2.history = [C, C, C, C, C, D, D, C, C, D]
+        self.assertEqual(P1.strategy(P2), 'D')
+

docs/reference/bibliography.rst

@@ -18,3 +18,4 @@ 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.
+.. [PRISON1998] LIFL (1998) PRISON. Available at: http://www.lifl.fr/IPD/ipd.frame.html (Accessed: 19 September 2016).