Merge pull request #686 from Axelrod-Python/mutual

Added strategies Desperate, Willing, Hopeless, Grim

Author: drvinceknight

axelrod/strategies/_strategies.py

@@ -43,6 +43,7 @@
     ZDGen2, ZDGTFT2, ZDSet2, WinStayLoseShift, WinShiftLoseStay)
 from .mindcontrol import MindController, MindWarper, MindBender
 from .mindreader import MindReader, ProtectedMindReader, MirrorMindReader
+from .mutual import Desperate, Hopeless, Willing
 from .oncebitten import OnceBitten, FoolMeOnce, ForgetfulFoolMeOnce, FoolMeForever
 from .prober import (Prober, Prober2, Prober3, HardProber,
                      NaiveProber, RemorsefulProber)
@@ -92,6 +93,7 @@
     Davis,
     Defector,
     DefectorHunter,
+    Desperate,
     DoubleCrosser,
     Eatherley,
     EventualCycleHunter,
@@ -115,21 +117,22 @@
     GoByMajority20,
     GoByMajority40,
     GoByMajority5,
+    Golden,
+    Gradual,
+    Grofman,
+    Grudger,
+    Grumpy,
     Handshake,
     HardGoByMajority,
     HardGoByMajority10,
     HardGoByMajority20,
     HardGoByMajority40,
     HardGoByMajority5,
-    Golden,
-    Gradual,
-    Grofman,
-    Grudger,
-    Grumpy,
     HardProber,
     HardTitFor2Tats,
     HardTitForTat,
     HesitantQLearner,
+    Hopeless,
     Inverse,
     InversePunisher,
     Joss,
@@ -184,6 +187,7 @@
     TrickyDefector,
     Tullock,
     TwoTitsForTat,
+    Willing,
     WinShiftLoseStay,
     WinStayLoseShift,
     ZDExtort2,

axelrod/strategies/mutual.py

@@ -0,0 +1,82 @@
+from axelrod import Actions, Player, init_args
+from axelrod.random_ import random_choice
+
+C, D = Actions.C, Actions.D
+
+
+class Desperate(Player):
+    """A player that only cooperates after mutual defection.
+
+    Names:
+
+    - Desperate: [Berg2015]_"""
+
+    name = "Desperate"
+    classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent):
+        if not opponent.history:
+            return random_choice()
+        if self.history[-1] == D and opponent.history[-1] == D:
+            return C
+        return D
+
+
+class Hopeless(Player):
+    """A player that only defects after mutual cooperation.
+
+    Names:
+
+    - Hopeless: [Berg2015]_"""
+
+    name = "Hopeless"
+    classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent):
+        if not opponent.history:
+            return random_choice()
+        if self.history[-1] == C and opponent.history[-1] == C:
+            return D
+        return C
+
+
+class Willing(Player):
+    """A player that only defects after mutual defection.
+
+    Names:
+
+    - Willing: [Berg2015]_"""
+
+    name = "Willing"
+    classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def strategy(self, opponent):
+        if not opponent.history:
+            return random_choice()
+        if self.history[-1] == D and opponent.history[-1] == D:
+            return D
+        return C

axelrod/tests/unit/test_mutual.py

@@ -0,0 +1,84 @@
+"""Tests for strategies Desperate, Hopeless, Willing, and Grim"""
+import axelrod
+
+from axelrod.random_ import seed
+
+from .test_player import TestPlayer
+
+C, D = axelrod.Actions.C, axelrod.Actions.D
+
+class TestDesperate(TestPlayer):
+
+    name = "Desperate"
+    player = axelrod.Desperate
+    expected_classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        seed(1)
+        self.first_play_test(C)
+        seed(2)
+        self.first_play_test(D)
+
+    def test_responses(self):
+        self.responses_test([C] * 4, [D] * 4, [D])
+        self.responses_test([D, D, C], [C, C, D], [D])
+        self.responses_test([D, D, D], [C, C, D], [C])
+
+
+class TestHopeless(TestPlayer):
+
+    name = "Hopeless"
+    player = axelrod.Hopeless
+    expected_classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        seed(1)
+        self.first_play_test(C)
+        seed(2)
+        self.first_play_test(D)
+
+    def test_responses(self):
+        self.responses_test([C] * 4, [D] * 4, [C])
+        self.responses_test([D] * 5, [C] * 5, [C])
+        self.responses_test([C, D, C], [C, C, C], [D])
+
+class TestWilling(TestPlayer):
+
+    name = "Willing"
+    player = axelrod.Willing
+    expected_classifier = {
+        'memory_depth': 1,
+        'long_run_time': False,
+        'stochastic': True,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+
+    def test_strategy(self):
+        seed(1)
+        self.first_play_test(C)
+        seed(2)
+        self.first_play_test(D)
+
+    def test_responses(self):
+        self.responses_test([C] * 4, [D] * 4, [C])
+        self.responses_test([D] * 5, [C] * 5, [C])
+        self.responses_test([C, C, D], [C, C, D], [D])

docs/tutorials/advanced/classification_of_strategies.rst

@@ -24,13 +24,13 @@ This allows us to, for example, quickly identify all the stochastic
 strategies::
 
     >>> len([s for s in axl.strategies if s().classifier['stochastic']])
-    40
+    43
 
 Or indeed find out how many strategy only use 1 turn worth of memory to
 make a decision::
 
     >>> len([s for s in axl.strategies if s().classifier['memory_depth']==1])
-    21
+    24
 
 We can also identify strategies that make use of particular properties of the
 tournament. For example, here is the number of strategies that  make use of the