Merge pull request #1 from Axelrod-Python/janga1997-SelfSteem

Fix strategy.

Author: janga1997

axelrod/strategies/selfsteem.py

@@ -1,8 +1,8 @@
 from axelrod.actions import Action, Actions
 from axelrod.player import Player
+from axelrod.random_ import random_choice
 
 from math import pi, sin
-import random
 
 C, D = Actions.C, Actions.D
 
@@ -12,21 +12,21 @@ class SelfSteem(Player):
     This strategy is based on the feeling with the same name.
     It is modeled on the sine curve(f = sin( 2* pi * n / 10 )), which varies
     with the current iteration.
-    
+
     If f > 0.95, 'ego' of the algorithm is inflated; always defects.
     If 0.95 > abs(f) > 0.3, rational behavior; follows TitForTat algortithm.
     If 0.3 > f > -0.3; random behavior.
     If f < -0.95, algorithm is at rock bottom; always cooperates.
-    
+
     Names:
-    
+
     - SelfSteem: [Andre2013]_
     """
 
     name = 'SelfSteem'
     classifier = {
-        'memory_depth': 1,
-        'stochastic': False,
+        'memory_depth': float("inf"),
+        'stochastic': True,
         'makes_use_of': set(),
         'long_run_time': False,
         'inspects_source': False,
@@ -36,18 +36,15 @@ class SelfSteem(Player):
 
     def strategy(self, opponent: Player) -> Action:
         turns_number = len(self.history)
-        sine_value =  sin( 2 * pi * turns_number / 10 )
-          
+        sine_value =  sin(2 * pi * turns_number / 10)
+
         if sine_value > 0.95:
             return D
-        
-        elif abs(sine_value) < 0.95 and abs(sine_value) > 0.3:
+
+        if abs(sine_value) < 0.95 and abs(sine_value) > 0.3:
             return opponent.history[-1]
-        
-        elif sine_value < 0.3 and sine_value > -0.3:
+
+        if sine_value < 0.3 and sine_value > -0.3:
             return random_choice()
-            
-        else:
-            return C
-            
-        
+
+        return C

axelrod/tests/unit/test_selfsteem.py

@@ -11,8 +11,8 @@ class TestSelfSteem(TestPlayer):
     name = "SelfSteem"
     player = axelrod.SelfSteem
     expected_classifier = {
-        'memory_depth': 1,
-        'stochastic': False,
+        'memory_depth': float("inf"),
+        'stochastic': True,
         'makes_use_of': set(),
         'inspects_source': False,
         'manipulates_source': False,
@@ -21,17 +21,18 @@ class TestSelfSteem(TestPlayer):
 
     def test_strategy(self):
         # Check for f > 0.95
-        self.responses_test([D], [C] * 2 , [C] * 2 )
+        self.responses_test([D], [C] * 2 , [C] * 2)
         self.responses_test([D], [C] * 13, [C] * 13)
-        
+
         # Check for f < -0.95
         self.responses_test([C], [C] * 7, [C] * 7)
         self.responses_test([C], [C] * 18, [D] * 18)
-        
+
+        # Check for -0.3 < f < 0.3
+        self.responses_test([C], [C] * 20, [C] * 20, seed=6)
+        self.responses_test([D], [C] * 20, [D] * 20, seed=5)
+
         # Check for 0.95 > abs(f) > 0.3
         self.responses_test([C], [C], [C])
         self.responses_test([D], [C] * 16, [D] * 16)
         self.responses_test([C], [D] * 9, [C] * 9)
-        
-        
-