Added Appold (k88r) strategy from Axelrod's second. (#1263)

* Fix bug in Kluepfel strategy

* Fix bug in Kluepfel strategy

* Add Appold strategy (k88r in Axelrod's second)

Author: gaffney2010

axelrod/strategies/_strategies.py

@@ -20,6 +20,7 @@
     UnnamedStrategy,
 )
 from .axelrod_second import (
+    Appold,
     Black,
     Borufsen,
     Cave,
@@ -248,6 +249,7 @@
     APavlov2006,
     APavlov2011,
     Appeaser,
+    Appold,
     ArrogantQLearner,
     AverageCopier,
     BackStabber,

axelrod/strategies/axelrod_second.py

@@ -2059,3 +2059,72 @@ def strategy(self, opponent: Player) -> Action:
             self.mode = "Coop Def Cycle 1"
         return D
 
+
+class Appold(Player):
+    """
+    Strategy submitted to Axelrod's second tournament by Scott Appold (K88R) and
+    came in 22nd in that tournament.
+
+    Cooperates for first four turns.
+
+    After four turns, will cooperate immediately following the first time the
+    opponent cooperates (starting with the opponent's fourth move).  Otherwise
+    will cooperate with probability equal to:
+
+    - If this strategy defected two turns ago, the portion of the time
+      (historically) that the opponent followed a defection with a cooperation.
+    - If this strategy cooperated two turns ago, the portion of the time
+      (historically) that the opponent followed a cooperation with a cooperation.
+      The opponent's first move is counted as a response to a cooperation.
+
+
+    Names:
+
+    - Appold: [Axelrod1980b]_
+    """
+
+    name = "Appold"
+    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 __init__(self) -> None:
+        super().__init__()
+
+        # Probability of a cooperation after an x is:
+        # opp_c_after_x / total_num_of_x.
+        self.opp_c_after_x = {C: 0, D: 1}
+        # This is the total counted, so it doesn't include the most recent.
+        self.total_num_of_x = {C: 0, D: 1}
+
+        self.first_opp_def = False
+
+    def strategy(self, opponent: Player) -> Action:
+        turn = len(self.history) + 1
+
+        us_two_turns_ago = C if turn <= 2 else self.history[-2]
+
+        # Update trackers
+        if turn > 1:
+            self.total_num_of_x[us_two_turns_ago] += 1
+        if turn > 1 and opponent.history[-1] == C:
+            self.opp_c_after_x[us_two_turns_ago] += 1
+
+        if turn <= 4:
+            return C
+
+        if opponent.history[-1] == D and not self.first_opp_def:
+            self.first_opp_def = True
+            return C
+
+        # Calculate the probability that the opponent cooperated last turn given
+        # what we know two turns ago.
+        prob_coop = self.opp_c_after_x[us_two_turns_ago] / self.total_num_of_x[
+            us_two_turns_ago]
+        return random_choice(prob_coop)

axelrod/tests/strategies/test_axelrod_second.py

@@ -1908,3 +1908,125 @@ def test_strategy(self):
         self.versus_test(custom_opponent, expected_actions=actions, attrs={
             "distrust_points": 2})  # But no more
 
+
+class TestAppold(TestPlayer):
+    name = "Appold"
+    player = axelrod.Appold
+    expected_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 test_strategy(self):
+        # Should cooperate 100% of the time with the cooperator
+        actions = [(C, C)] * 100
+        self.versus_test(axelrod.Cooperator(), expected_actions=actions)
+
+        opponent = axelrod.Defector()
+        # Cooperate always the first 4 turns
+        actions = [(C, D)] * 4
+        # Should cooperate because we forgive the first_opp_def after the fourth
+        # turn.
+        actions += [(C, D)]
+        # Own move two turns ago is C, so D.
+        actions += [(D, D)]
+        # Then defect most of the time, depending on the random number.  We
+        # don't defect 100% of the time, because of the way that initialize
+        # opp_c_after_x.
+        actions += [(D, D),
+                    (C, D),
+                    (D, D),
+                    (D, D),  # C can never be two moves after a C.
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (C, D),
+                    (C, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (C, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (D, D),
+                    (C, D),
+                    (C, D),
+                    (D, D),
+                    (D, D)]
+        self.versus_test(opponent, expected_actions=actions, seed=1,
+                         attrs={"first_opp_def": True})
+
+        # An opponent who defects for a long time, then tries cooperating
+        opponent_actions = [C] * 30 + [D] + [C] * 10
+        MostlyCooperates = axelrod.MockPlayer(actions=opponent_actions)
+        # Cooperate always at first
+        actions = [(C, C)] * 30
+        # The opponent defects once
+        actions += [(C, D)]
+        # But we forgive it.
+        actions += [(C, C)] * 10
+        self.versus_test(MostlyCooperates, expected_actions=actions)
+
+        opponent = axelrod.CyclerDC()
+        # First three opponent actions get counted as reactions to C.  Fourth
+        # action will get counted on next turn.
+        actions = [(C, D), (C, C), (C, D), (C, C)]
+        self.versus_test(opponent, expected_actions=actions,
+                         attrs={"opp_c_after_x": {C: 1, D: 1},
+                                "total_num_of_x": {C: 3, D: 1}})
+        # Will cooperate 50% of the time
+        actions += [(C, D)]
+        self.versus_test(opponent, expected_actions=actions,
+                         attrs={"opp_c_after_x": {C: 2, D: 1},
+                                "total_num_of_x": {C: 4, D: 1},
+                                "first_opp_def": False}, seed=100)
+        # Always cooperate, because we forgive the first defect
+        actions += [(C, C)]
+        self.versus_test(opponent, expected_actions=actions,
+                         attrs={"first_opp_def": True}, seed=100)
+
+        # Against a random opponent, will respond mostly randomly too.
+        actions = [(C, C),
+                   (C, C),
+                   (C, D),
+                   (C, C),
+                   (C, C),
+                   (C, D),
+                   (C, C),
+                   (C, C),
+                   (C, C),
+                   (D, C),
+                   (C, D),
+                   (D, D),
+                   (C, D),
+                   (C, D),
+                   (C, C),
+                   (C, C),
+                   (D, C),
+                   (C, D),
+                   (D, D),
+                   (C, C),
+                   (C, D),
+                   (C, C),
+                   (C, C),
+                   (C, D),
+                   (D, C),
+                   (C, D),
+                   (D, D),
+                   (C, D),
+                   (C, C),
+                   (D, C)]
+        self.versus_test(axelrod.Random(0.5), expected_actions=actions, seed=7)

docs/reference/overview_of_strategies.rst

@@ -115,7 +115,7 @@ repository.
    "K85R_", "Robert B Falk and James M Langsted", "Not Implemented"
    "K86R_", "Bernard Grofman", "Not Implemented"
    "K87R_", "E E H Schurmann",  "Not Implemented"
-   "K88R_", "Scott Appold", "Not Implemented"
+   "K88R_", "Scott Appold", ":class:`Appold <axelrod.strategies.axelrod_second.Appold>`"
    "K89R_", "Gene Snodgrass", "Not Implemented"
    "K90R_", "John Maynard Smith", "Not Implemented"
    "K91R_", "Jonathan Pinkley", "Not Implemented"

docs/tutorials/advanced/classification_of_strategies.rst

@@ -47,7 +47,7 @@ strategies::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    86
+    87
 
 Or, to find out how many strategies only use 1 turn worth of memory to
 make a decision::