Implemented Black, K83R from Axelrod's Second (#1155)

* Implemented Black, K83R from Axelrod's Second.

* Fixed docstrings.

Author: gaffney2010

axelrod/strategies/_strategies.py

@@ -10,7 +10,7 @@
 from .axelrod_second import (
     Champion, Eatherley, Tester, Gladstein, Tranquilizer, MoreGrofman,
     Kluepfel, Borufsen, Cave, WmAdams, GraaskampKatzen, Weiner, Harrington,
-    MoreTidemanAndChieruzzi, Getzler, Leyvraz, White)
+    MoreTidemanAndChieruzzi, Getzler, Leyvraz, White, Black)
 from .backstabber import BackStabber, DoubleCrosser
 from .better_and_better import BetterAndBetter
 from .bush_mosteller import BushMosteller
@@ -107,8 +107,9 @@
     Appeaser,
     ArrogantQLearner,
     AverageCopier,
-    BetterAndBetter,
     BackStabber,
+    BetterAndBetter,
+    Black,
     Borufsen,
     Bully,
     BushMosteller,

axelrod/strategies/axelrod_second.py

@@ -1545,3 +1545,55 @@ def strategy(self, opponent: Player) -> Action:
         if np.floor(np.log(turn)) * opponent.defections >= turn:
             return D
         return C
+
+
+class Black(Player):
+    """
+    Strategy submitted to Axelrod's second tournament by Paul E Black (K83R)
+    and came in fourteenth in that tournament.
+
+    The strategy Cooperates for the first five turns.  Then it calculates the
+    number of opponent defects in the last five moves and Cooperates with
+    probability `prob_coop`[`number_defects`], where:
+
+    prob_coop[number_defects] = 1 - (number_defects^ 2 - 1) / 25
+
+    Names:
+
+    - Black: [Axelrod1980b]_
+    """
+
+    name = 'Black'
+    classifier = {
+        'memory_depth': 5,
+        '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__()
+        # Maps number of opponent defects from last five moves to own
+        # Cooperation probability
+        self.prob_coop = {
+            0: 1.0,
+            1: 1.0,
+            2: 0.88,
+            3: 0.68,
+            4: 0.4,
+            5: 0.04
+        }
+
+    def strategy(self, opponent: Player) -> Action:
+        if len(opponent.history) < 5:
+            return C
+
+        recent_history = opponent.history[-5:]
+
+        did_d = np.vectorize(lambda action: int(action == D))
+        number_defects = np.sum(did_d(recent_history))
+
+        return random_choice(self.prob_coop[number_defects])

axelrod/tests/strategies/test_axelrod_second.py

@@ -991,3 +991,29 @@ def test_strategy(self):
                    (C, C), (C, D), (C, C), (C, D), (C, C), (C, D)]
         self.versus_test(axelrod.Random(0.5), expected_actions=actions, seed=12)
 
+
+class TestBlack(TestPlayer):
+    name = 'Black'
+    player = axelrod.Black
+    expected_classifier = {
+        'memory_depth': 5,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        actions = [(C, C)] * 30
+        self.versus_test(axelrod.Cooperator(), expected_actions=actions)
+
+        actions = [(C, D)] * 5
+        actions += [(D, D), (D, D), (D, D), (D, D), (D, D), (D, D), (D, D), (D, D), (D, D), (C, D)]
+        self.versus_test(axelrod.Defector(), expected_actions=actions, seed=1)
+
+        actions = [(C, D)] * 5
+        actions += [(D, D), (C, D), (D, D), (D, D), (D, D), (C, D), (D, D), (D, D), (D, D), (D, D)]
+        self.versus_test(axelrod.Defector(), expected_actions=actions, seed=15)
+

docs/reference/overview_of_strategies.rst

@@ -152,7 +152,7 @@ repository.
    "K80R_", "Robyn M Dawes and Mark Batell", Not Implemented
    "K81R_", "Martyn Jones", "Not Implemented"
    "K82R_", "Robert A Leyland", "Not Implemented"
-   "K83R_", "Paul E Black", "Not Implemented"
+   "K83R_", "Paul E Black", ":class:`White <axelrod.strategies.axelrod_second.White>`"
    "K84R_", "T Nicolaus Tideman and Paula Chieruzzi", ":class:`More Tideman And Chieruzzi <axelrod.strategies.axelrod_second.MoreTidemanAndChieruzzi>`"
    "K85R_", "Robert B Falk and James M Langsted", "Not Implemented"
    "K86R_", "Bernard Grofman", "Not Implemented"

docs/tutorials/advanced/classification_of_strategies.rst

@@ -47,7 +47,7 @@ strategies::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    81
+    82
 
 Or, to find out how many strategies only use 1 turn worth of memory to
 make a decision::