Adjust doctests.

Author: drvinceknight

docs/tutorials/advanced/classification_of_strategies.rst

@@ -47,7 +47,7 @@ strategies::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    87
+    88
 
 Or, to find out how many strategies only use 1 turn worth of memory to
 make a decision::
@@ -57,7 +57,7 @@ make a decision::
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    31
+    32
 
 Multiple filters can be specified within the filterset dictionary. To specify a
 range of memory_depth values, we can use the 'min_memory_depth' and
@@ -69,7 +69,7 @@ range of memory_depth values, we can use the 'min_memory_depth' and
     ... }
     >>> strategies = axl.filtered_strategies(filterset)
     >>> len(strategies)
-    54
+    55
 
 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

docs/tutorials/advanced/player_information.rst

@@ -4,13 +4,12 @@ Player information
 ==================
 
 It is possible to determine what information players know about their matches.
-By default all known information is given.
-For example let us create a match with 5 turns between :code:`SteinAndRapoport`
-and :code:`Alternator`. The latter of these two always defects on the last 2
-turns::
+By default all known information is given.  For example let us create a match
+with 5 turns between :code:`FirstBySteinAndRapoport` and :code:`Alternator`. The
+latter of these two always defects on the last 2 turns::
 
     >>> import axelrod as axl
-    >>> players = (axl.Alternator(), axl.SteinAndRapoport())
+    >>> players = (axl.Alternator(), axl.FirstBySteinAndRapoport())
     >>> axl.Match(players, turns=5).play()
     [(C, C), (D, C), (C, C), (D, D), (C, D)]
 

docs/tutorials/contributing/strategy/classifying_the_new_strategy.rst

@@ -26,8 +26,8 @@ default classifier dictionary from the class. This might sometimes be modified b
 the initialisation depending on input parameters. A good example of this is the
 :code:`Joss` strategy::
 
-    >>> joss = axelrod.Joss()
-    >>> boring_joss = axelrod.Joss(p=1)
+    >>> joss = axelrod.FirstByJoss()
+    >>> boring_joss = axelrod.FirstByJoss(p=1)
     >>> joss.classifier['stochastic'], boring_joss.classifier['stochastic']
     (True, False)