More tests for mutual, mark as stochastic

Author: marcharper

axelrod/strategies/mutual.py

@@ -3,12 +3,13 @@
 
 C, D = Actions.C, Actions.D
 
+
 class Desperate(Player):
     """A player that only cooperates after mutual defection"""
     name = "Desperate"
     classifier = {
         'memory_depth': 1,
-        'stochastic': False,
+        'stochastic': True,
         'makes_use_of': set(),
         'inspects_source': False,
         'manipulates_source': False,
@@ -22,12 +23,13 @@ def strategy(self, opponent):
             return C
         return D
 
+
 class Hopeless(Player):
     """A player that only defects after mutual cooperation"""
     name = "Hopeless"
     classifier = {
         'memory_depth': 1,
-        'stochastic': False,
+        'stochastic': True,
         'makes_use_of': set(),
         'inspects_source': False,
         'manipulates_source': False,
@@ -41,12 +43,13 @@ def strategy(self, opponent):
             return D
         return C
 
+
 class Willing(Player):
     """A player that only defects after mutual defection"""
     name = "Willing"
     classifier = {
         'memory_depth': 1,
-        'stochastic': False,
+        'stochastic': True,
         'makes_use_of': set(),
         'inspects_source': False,
         'manipulates_source': False,

axelrod/tests/unit/test_mutual.py

@@ -1,6 +1,8 @@
 """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
@@ -11,17 +13,19 @@ class TestDesperate(TestPlayer):
     player = axelrod.Desperate
     expected_classifier = {
         'memory_depth': 1,
-        'stochastic': 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)
 
     def test_responses(self):
-
-        self.responses_test([C]* 4, [D] * 4, [D])
+        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])
 
@@ -32,16 +36,18 @@ class TestHopeless(TestPlayer):
     player = axelrod.Hopeless
     expected_classifier = {
         'memory_depth': 1,
-        'stochastic': 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)
 
     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])
@@ -52,16 +58,18 @@ class TestWilling(TestPlayer):
     player = axelrod.Willing
     expected_classifier = {
         'memory_depth': 1,
-        'stochastic': 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)
 
     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

@@ -24,7 +24,7 @@ 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::