Add Alexei strategy

axelrod/strategies/_strategies.py

@@ -75,7 +75,7 @@
     TitForTat, TitFor2Tats, TwoTitsForTat, Bully, SneakyTitForTat,
     SuspiciousTitForTat, AntiTitForTat, HardTitForTat, HardTitFor2Tats,
     OmegaTFT, Gradual, ContriteTitForTat, SlowTitForTwoTats, AdaptiveTitForTat,
-    SpitefulTitForTat, SlowTitForTwoTats2)
+    SpitefulTitForTat, SlowTitForTwoTats2, Alexei)
 from .verybad import VeryBad
 from .worse_and_worse import (WorseAndWorse, KnowledgeableWorseAndWorse,
                               WorseAndWorse2, WorseAndWorse3)
@@ -86,6 +86,7 @@
     Adaptive,
     AdaptiveTitForTat,
     Aggravater,
+    Alexei,
     ALLCorALLD,
     Alternator,
     AlternatorHunter,

axelrod/strategies/titfortat.py

@@ -1,6 +1,6 @@
 from axelrod.actions import Actions, Action
 from axelrod.player import Player
-from axelrod.strategy_transformers import TrackHistoryTransformer
+from axelrod.strategy_transformers import TrackHistoryTransformer, FinalTransformer
 
 C, D = Actions.C, Actions.D
 
@@ -636,3 +636,31 @@ def strategy(self, opponent: Player) -> Action:
 
         # Otherwise play previous move
         return self.history[-1]
+
+@FinalTransformer((D,), name_prefix=None)
+class Alexei(Player):
+    """
+    Plays similar to Tit-for-Tat, but always defect on last turn.
+
+    Names:
+
+    - From http://lesswrong.com/lw/7f2/prisoners_dilemma_tournament_results/    
+    """
+
+    name = 'Alexei'
+    classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': False,
+        'makes_use_of': {'length'},
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent: Player) -> Action:
+        if not self.history:
+            return C
+        if opponent.history[-1] == D:
+            return D
+        return C

axelrod/tests/strategies/test_titfortat.py

@@ -563,3 +563,52 @@ def test_strategy(self):
         actions = [(C, C), (C, C), (C, D), (C, D), (D, C), (D, D), (D, D),
                     (D, C), (D, C), (C, D), (C, D)]
         self.versus_test(opponent, expected_actions=actions)
+
+class TestAlexei(TestPlayer):
+    """
+    Tests for the Alexei strategy
+    """
+
+    name = "Alexei: ('D',)"
+    player = axelrod.Alexei
+    expected_classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': False,
+        'makes_use_of': {'length'},
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        self.first_play_test(C)
+        self.second_play_test(rCC=C, rCD=D, rDC=C, rDD=D)
+
+        actions = [(C, C), (C, D), (D, C), (C, D), (D, C)]
+        self.versus_test(axelrod.Alternator(), expected_actions=actions)
+
+        actions = [(C, C), (C, C), (C, C), (C, C), (D, C)]
+        self.versus_test(axelrod.Cooperator(), expected_actions=actions)
+
+        actions = [(C, D), (D, D), (D, D), (D, D), (D, D)]
+        self.versus_test(axelrod.Defector(), expected_actions=actions)
+
+        actions = [(C, C), (C, D), (D, C), (C, D), (D, C)]
+        self.versus_test(axelrod.Alternator(), expected_actions=actions,
+                         match_attributes={"length": -1})
+
+        actions = [(C, D), (D, D), (D, C), (C, C), (D, D)]
+        self.versus_test(axelrod.Random(), expected_actions=actions,
+                         seed=0)
+
+        actions = [(C, C), (C, D), (D, D), (D, C)]
+        self.versus_test(axelrod.Random(), expected_actions=actions,
+                         seed=1)
+
+        opponent = axelrod.MockPlayer(actions=[C, D])
+        actions = [(C, C), (C, D), (D, C), (D, D)]
+        self.versus_test(opponent, expected_actions=actions)
+
+        opponent = axelrod.MockPlayer(actions=[C, C, D, D, C, D])
+        actions = [(C, C), (C, C), (C, D), (D, D), (D, C), (D, D)]
+        self.versus_test(opponent, expected_actions=actions)

docs/reference/bibliography.rst

@@ -20,6 +20,7 @@ documentation.
 .. [Frean1994] Frean, Marcus R. “The Prisoner's Dilemma without Synchrony.” Proceedings: Biological Sciences, vol. 257, no. 1348, 1994, pp. 75–79. www.jstor.org/stable/50253.
 .. [Hilde2013] Hilbe, C., Nowak, M.A. and Traulsen, A. (2013). Adaptive dynamics of extortion and compliance, PLoS ONE, 8(11), p. e77886. doi: 10.1371/journal.pone.0077886.
 .. [Kraines1989] Kraines, D. & Kraines, V. Theor Decis (1989) 26: 47.  doi:10.1007/BF00134056
+.. [LessWrong2011] Zoo of Strategies (2011) LessWrong. Available at: http://lesswrong.com/lw/7f2/prisoners_dilemma_tournament_results/ 
 .. [Li2009] Li, J. & Kendall, G. (2009). A Strategy with Novel Evolutionary Features for the Iterated Prisoner’s Dilemma. Evolutionary Computation 17(2): 257–274.
 .. [Li2011] Li, J., Hingston, P., Member, S., & Kendall, G. (2011). Engineering Design of Strategies for Winning Iterated Prisoner ’ s Dilemma Competitions, 3(4), 348–360.
 .. [Li2014] Li, J. and Kendall, G. (2016). The Effect of Memory Size on the Evolutionary Stability of Strategies in Iterated Prisoner's Dilemma. IEEE Transactions on Evolutionary Computation, 18(6) 819-826

docs/tutorials/advanced/classification_of_strategies.rst

@@ -80,7 +80,7 @@ length of each match of the tournament::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    25
+    26
 
 Note that in the filterset dictionary, the value for the 'makes_use_of' key
 must be a list. Here is how we might identify the number of strategies that use